FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Dec 2010 09:02:26 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293526805j3pyvc4jozcrbd2.htm/, Retrieved Sun, 05 May 2024 01:38:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116235, Retrieved Sun, 05 May 2024 01:38:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-26 20:18:12] [dcd1a35a8985187cb1e9de87792355b2]
-   P   [Multiple Regression] [] [2010-12-26 20:25:18] [dcd1a35a8985187cb1e9de87792355b2]
-   PD    [Multiple Regression] [] [2010-12-26 20:50:02] [dcd1a35a8985187cb1e9de87792355b2]
-             [Multiple Regression] [] [2010-12-28 09:02:26] [4cd8ab5c565aadc5ce193fb2faa1c53a] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22	27	5	26	49	35
23	36	4	25	45	34
27	25	4	17	54	13
19	27	3	37	36	35
15	25	3	35	36	28
29	44	3	15	53	32
25	50	4	27	46	35
25	41	4	36	42	36
21	48	5	25	41	27
22	43	4	30	45	29
22	47	2	27	47	27
24	41	3	33	42	28
22	44	2	29	45	29
23	47	5	30	40	28
19	40	3	25	45	30
19	46	3	23	40	25
21	28	3	26	42	15
20	56	3	24	45	33
23	49	4	35	47	31
11	25	4	39	31	37
21	41	4	23	46	37
19	26	3	32	34	34
21	50	5	29	43	32
23	47	4	26	45	21
19	52	2	21	42	25
22	37	5	35	51	32
19	41	3	23	44	28
23	45	4	21	47	22
29	26	4	28	47	25
27		3	30	41	26
18	52	4	21	44	34
30	46	2	29	51	34
26	58	3	28	46	36
20	54	5	19	47	36
22	29	3	26	46	26
20	50	3	33	38	26
21	43	2	34	50	34
18	30	3	33	48	33
21	47	2	40	36	31
27	45	3	24	51	33
	48	1	35	35	22
18	48	3	35	49	29
24	26	4	32	38	24
24	46	5	20	47	37
17		3	35	36	32
22	50	3	35	47	23
21	25	4	21	46	29
23	47	2	33	43	35
19	47	2	40	53	20
22	41	3	22	55	28
19	45	2	35	39	26
24	41	4	20	55	36
22	45	5	28	41	26
26	40	3	46	33	33
22	29	4	18	52	25
23	34	5	22	42	29
27	45	5	20	56	32
21	52	3	25	46	35
16	41	4	31	33	24
21	48	3	21	51	31
18	45	3	23	46	29
25	54	2	26	46	27
20	25	3	34	50	29
24	26	4	31	46	29
20	28	4	23	51	27
24	50	4	31	48	34
23	48	4	26	44	32
23	51	3	36	38	31
22	53	3	28	42	31
22	37	3	34	39	31
20	56	2	25	45	16
14	43	3	33	31	25
21	34	3	46	29	27
23	42	3	24	48	32
17	32	3	32	38	28
25	31	5	33	55	25
10	46	3	42	32	25
25	30	5	17	51	36
23	47	4	36	53	36
27	33	4	40	47	36
16	25	4	30	45	27
19	25	5	19	33	29
23	21	4	33	49	32
19	36	5	35	46	29
19	50	3	23	42	31
26	48	3	15	56	34
19	48	2	38	35	27
22	25	3	37	40	28
21	48	4	23	44	32
22	49	5	41	46	33
20	27	5	34	46	29
20	28	3	38	39	32
20	43	2	45	35	35
21	48	3	27	48	33
21	48	4	46	42	27
14	25	1	26	39	16
28	49	4	44	39	32
24	26	3	36	41	26
24	51	3	20	52	32
24	25	4	44	45	38
19	29	3	27	42	24
19	29	4	27	44	26
14	43	2	41	33	19
29	46	3	30	42	37
22	44	3	33	46	25
21	25	3	37	45	24
15	51	2	30	40	23
23	42	5	20	48	28
24	53	5	44	32	38
20	25	4	20	53	28
25	49	2	33	39	28
25	51	3	31	45	26
19	20	3	23	36	21
23	44	3	33	38	35
22	38	4	33	49	31
19	46	5	32	46	34
24	42	4	25	43	30
21	29		22	37	30
19	46	4	16	48	24
21	49	2	36	45	27
18	51	3	35	32	26
24	38	3	25	46	30
7	41	1	27	20	15
24	47	3	32	42	28
24	44	3	36	45	34
23	47	3	51	29	29
24	46	3	30	51	26
27	44	4	20	55	31
20	28	3	29	50	28
20	47	4	26	44	33
22	28	4	20	41	32
19	41	5	40	40	33
18	45	4	29	47	31
14	46	4	32	42	37
24	46	4	33	40	27
29	22	3	32	51	19
25	33	3	34	43	27
24	41	4	24	45	31
20	47	5	25	41	38
18	25	3	41	41	22
25	42	3	39	37	35
21	47	3	21	46	35
21	50	3	38	38	30
21	55	5	28	39	41
23	21	3	37	45	25
18		3	26	46	28
23	52	3	30	39	45
13	49	4	25	21	21
23	46	4	38	31	33
17		4	31	35	25
24	45	3	31	49	29
16	52	3	27	40	31
23		3	21	45	29
20	40	4	26	46	31
24	49	4	37	45	31
15	38	5	28	34	25
20	32	5	29	41	27
27	46	4	33	43	26
27	32	3	41	45	26
19	41	3	19	48	23
22	43	3	37	43	27
16	44	4	36	45	24
21	47	5	27	45	35
18	28	3	33	34	24
22	52	1	29	40	32
18	27	2	42	40	24
24	45	5	27	55	24
24	27	4	47	44	38
19	25	4	17	44	36
26	28	4	34	48	24
28	25	3	32	51	18
23	52	4	25	49	34
22	44	3	27	33	23
20	43	3	37	43	35
20	47	4	34	44	22
27	52	4	27	44	34
19	40	2	37	41	28
23	42	3	32	45	34
19	45	5	26	44	32
21	45	2	29	44	24
13	50	5	28	40	34
18	49	3	19	48	33
19	52	2	46	49	33
23	48	3	31	46	29
30	51	3	42	49	38
22	49	4	33	55	24
23	31	4	39	51	25
22	43	3	27	46	37
22	31	3	35	37	33
23	28	4	23	43	30
27	43	4	32	41	22
23	31	3	22	45	28
18	51	3	17	39	24
24	58	4	35	38	33
19	25	5	34	41	37

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Behoefte_affiliatie[t] = + 59.8331365247396 -0.321873599043689leeftijd[t] -0.0894242784873792opleiding[t] + 0.0106074503726083Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Behoefte_affiliatie[t] =  +  59.8331365247396 -0.321873599043689leeftijd[t] -0.0894242784873792opleiding[t] +  0.0106074503726083Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Behoefte_affiliatie[t] =  +  59.8331365247396 -0.321873599043689leeftijd[t] -0.0894242784873792opleiding[t] +  0.0106074503726083Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Behoefte_affiliatie[t] = + 59.8331365247396 -0.321873599043689leeftijd[t] -0.0894242784873792opleiding[t] + 0.0106074503726083Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)59.83313652473967.0645928.469400
leeftijd-0.3218735990436890.062584-5.14311e-060
opleiding-0.08942427848737920.078225-1.14320.2544120.127206
Neuroticisme0.01060745037260830.0911740.11630.9075040.453752
Extraversie-0.30954065948540.058358-5.304200
`Openheid `-0.3053207323965170.074876-4.07776.7e-053.3e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 59.8331365247396 & 7.064592 & 8.4694 & 0 & 0 \tabularnewline
leeftijd & -0.321873599043689 & 0.062584 & -5.1431 & 1e-06 & 0 \tabularnewline
opleiding & -0.0894242784873792 & 0.078225 & -1.1432 & 0.254412 & 0.127206 \tabularnewline
Neuroticisme & 0.0106074503726083 & 0.091174 & 0.1163 & 0.907504 & 0.453752 \tabularnewline
Extraversie & -0.3095406594854 & 0.058358 & -5.3042 & 0 & 0 \tabularnewline
`Openheid
` & -0.305320732396517 & 0.074876 & -4.0777 & 6.7e-05 & 3.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]59.8331365247396[/C][C]7.064592[/C][C]8.4694[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.321873599043689[/C][C]0.062584[/C][C]-5.1431[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.0894242784873792[/C][C]0.078225[/C][C]-1.1432[/C][C]0.254412[/C][C]0.127206[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.0106074503726083[/C][C]0.091174[/C][C]0.1163[/C][C]0.907504[/C][C]0.453752[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.3095406594854[/C][C]0.058358[/C][C]-5.3042[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Openheid
`[/C][C]-0.305320732396517[/C][C]0.074876[/C][C]-4.0777[/C][C]6.7e-05[/C][C]3.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)59.83313652473967.0645928.469400
leeftijd-0.3218735990436890.062584-5.14311e-060
opleiding-0.08942427848737920.078225-1.14320.2544120.127206
Neuroticisme0.01060745037260830.0911740.11630.9075040.453752
Extraversie-0.30954065948540.058358-5.304200
`Openheid `-0.3053207323965170.074876-4.07776.7e-053.3e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.601830538297624
R-squared0.362199996827607
Adjusted R-squared0.345326980870666
F-TEST (value)21.466227362785
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.32437127245557
Sum Squared Residuals16432.3970294264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.601830538297624 \tabularnewline
R-squared & 0.362199996827607 \tabularnewline
Adjusted R-squared & 0.345326980870666 \tabularnewline
F-TEST (value) & 21.466227362785 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.32437127245557 \tabularnewline
Sum Squared Residuals & 16432.3970294264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.601830538297624[/C][/ROW]
[ROW][C]R-squared[/C][C]0.362199996827607[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.345326980870666[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.466227362785[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.32437127245557[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16432.3970294264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.601830538297624
R-squared0.362199996827607
Adjusted R-squared0.345326980870666
F-TEST (value)21.466227362785
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.32437127245557
Sum Squared Residuals16432.3970294264






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12225.117503719148-3.11750371914802
22323.8429415262079-0.842941526207933
32730.9245609576659-3.92456095766588
41929.4370628035319-10.4370628035319
51532.1968402276497-17.1968402276497
62919.38561869752959.61438130247047
72518.74306464845966.25693535154043
82522.66823599875132.33176400124867
92123.2664418239135-2.26644182391349
102223.1694672467477-1.16946724674772
112222.0205592022521-0.0205592022521363
122425.168403785293-1.16840378529302
132223.0158347543062-1.01583475430618
142323.6455726019091-0.645572601909105
151923.8661543381066-4.86615433810661
161924.9880048025088-5.98800480250884
172133.2476779414074-12.2476779414074
182017.78960710584542.21039289415457
192320.06154012058482.9384598794152
201130.949662456511-19.949662456511
212120.98685577356930.0131442264306973
221930.6303012020798-11.6303012020798
232120.51943944636320.480560553636838
242324.2821089082547-1.28210890825467
251922.5058912670181-3.50589126701808
262222.2911156602836-0.291115660283565
271924.4432479625961-5.44324796259614
282323.9484168031117-0.948416803111689
292929.2223051403605-0.222305140360478
302743.0758625085064-16.0758625085064
315237.450455702325514.5495442976745
324638.67434375470827.3256562452918
335839.601700257697118.3982997423029
345439.162737551575814.8372624484242
352942.8759554095259-13.8759554095259
365041.85980512473698.14019487526314
374340.65918077107082.34081922892917
383039.7990950120652-9.79909501206515
394738.15486618181768.84513381818242
404532.392076094863412.6079239051366
41125.4439476935796-24.4439476935796
42329.1260721031755-26.1260721031755
43424.9159080638744-20.9159080638744
44543.4070457103042-38.4070457103042
453529.22447878430855.7755212156915
463533.91127600558021.08872399441979
472127.5185657905591-6.51856579055908
483327.90521111527545.09478888472456
494027.611584877782912.3884151222171
502225.2877791951336-3.28777919513363
513531.2971637618193.70283623818099
522023.6882451211629-3.68824512116287
532831.2894928563588-3.28949285635877
544636.29570841974919.70429158025093
551829.0530876863671-11.0530876863671
562228.5516091100055-6.55160911000551
572022.1573180340913-2.15731803409132
582528.1543704591459-3.15437045914591
593131.5139676779368-0.513967677936793
602125.9910725730771-4.99107257307712
612325.3729960749065-2.3729960749065
622634.1701657726983-8.1701657726983
633432.13139122915731.8686087708427
643132.7573745048708-1.75737450487082
652324.5593903594388-1.55939035943877
663125.50751507575645.49248492424363
672626.3505567828462-0.350556782846201
683627.74153388625238.2584661137477
692831.4066900418439-3.40669004184395
703426.77514414040377.22485585959629
712529.8403298621303-4.8403298621303
723336.4018598303324-3.40185983033239
734634.41164809630711.588351903693
742430.8806902146584-6.8806902146584
753234.240882116718-2.24088211671803
763324.84572358536048.15427641463962
774236.74993720592775.2500627940723
781724.6725863811359-7.67258638113595
793628.40483821733467.5951617826654
804032.69572313338127.30427686661876
813033.8707904565763-3.87079045657634
821939.1403382599544-20.1403382599544
833328.73122741362534.26877258637471
843526.24219327821598.75780672178407
852328.044172588995-5.04417258899498
861523.5007379467095-8.50073794670946
873837.73199026292410.268009737075917
883728.59783463828498.40216536171506
892326.3483891866514-3.34838918665136
904132.40389731801028.59610268198982
913433.06269523726730.937304762732664
923830.70974843522657.29025156477346
934529.886553416488215.1134465835118
942725.57572445349851.42427554650147
954636.00465693143049.99534306856957
962629.7575110723-3.75751107230004
974435.70904871557238.29095128442775
983627.86333070127428.13666929872585
992031.5289118550932-11.5289118550932
1004432.259602220066611.7403977799334
1012734.1718421836245-7.17184218362446
1022729.5732814086967-2.57328140869672
1034132.66502999232548.33497000767465
1043028.70335975452191.29664024547813
1053334.3596217800457-1.35962178004566
1063726.964538541117210.0354614588828
1073030.6180941559829-0.618094155982933
1082024.2016441672297-4.20164416722974
1094437.38740836355226.61259163644775
1102024.7570084575151-4.75700845751505
1113328.33883679275944.66116320724062
1123136.1185594972838-5.11855949728375
1132332.0759972549547-9.07599725495468
1143331.72162593218731.27837406781273
1153325.72524509725877.27475490274133
1163228.01911368112883.98088631887124
1172527.8388646312644-2.8388646312644
1183742.8425156230228-5.84251562302283
1194839.13939768246858.86060231753151
1204538.45904469445596.5409553055441
1213241.1596830916966-9.15968309169657
1224641.43266363510294.56733636489714
1232042.658514607755-22.658514607755
1244237.22105253948324.77894746051684
1254530.831246588878514.1687534111215
1262938.7523182355638-9.75231823556379
1275142.17211796096738.82788203903274
1285538.580654777115516.4193452228845
1295040.35423866903039.64576133096973
1304440.19640495413663.80359504586339
1314134.50849293607066.4915070639294
1324037.98654213285182.01345786714818
1334738.08263169807188.91736830192823
1344234.95182658653937.04817341346067
1354038.08372376747861.91627623252136
1365140.522450163083310.4775498369167
1374340.86541191668182.13458808331817
1384539.38439794481035.61560205518968
1394132.81071700090848.1892829990916
1404138.12569275732312.87430724267686
1413739.8478435187051-2.84784351870506
1424634.689213419082111.3107865809179
1433838.7857446711579-0.785744671157946
1443932.57682793943616.42317206056392
1454528.115671050132216.8843289498678
1462826.61807526848271.38192473151731
1474537.15916802531867.84083197468137
1482127.1314689730685-6.13146897306851
1493335.8654777966963-2.8654777966963
1502421.38758913957872.61241086042127
1511621.267268615358-5.26726861535805
1522342.383847373134-19.383847373134
1534039.78509558346910.214904416530853
1544941.5387076613047.46129233869598
1553842.2356109094779-4.23561090947787
1563239.4641123378523-7.46411233785233
1574639.75904438317716.24095561682288
1583241.8293045142391-9.82930451423912
1594143.8411207733459-2.84112077334594
1604342.77220826514760.227791734852425
1614441.96299216181012.0370078381899
1624739.9569520120037.04304798799703
1632842.1311359098207-14.1311359098207
1645241.941182577796110.0588174222039
1652741.1011942399206-14.1011942399206
1664541.63604937568943.36395062431065
1672737.2457898800738-10.2457898800737
1682538.4103544268903-13.4103544268903
1692840.0364179431271-12.0364179431271
1702543.9528100691583-18.9528100691583
1715239.588361699411212.4116383005888
1724443.57725625465080.422743745349184
1734339.07460005967833.92539994032169
1744740.91839019300396.08160980699606
1755240.272438087762911.7275619122371
1764040.626081177185-0.626081177185027
1774240.15779774474231.84220225525766
1784540.04842862138444.95157137861561
1794545.6646677181087-0.664667718108656
1805040.12403114113799.87596885886205
1814941.66167637568157.33832362431851
1825238.358418976352513.6415810236475
1834838.44700471466769.55299528533236
1845137.151859926227813.8481400737722
1854941.72669803620537.27330196379472
1863141.1435026367017-10.1435026367017
1874338.77094241190614.22905758809391
1883138.8929230361987-7.89292303619866
1892839.4151245301093-11.4151245301093
1904342.28669932844690.713300671553054
1913143.214605218925-12.214605218925
1925143.00432015268887.99567984731121
1935839.802939817113718.1970601828863
1942537.448188012544-12.448188012544
1952738.5622024299982-11.5622024299982

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22 & 25.117503719148 & -3.11750371914802 \tabularnewline
2 & 23 & 23.8429415262079 & -0.842941526207933 \tabularnewline
3 & 27 & 30.9245609576659 & -3.92456095766588 \tabularnewline
4 & 19 & 29.4370628035319 & -10.4370628035319 \tabularnewline
5 & 15 & 32.1968402276497 & -17.1968402276497 \tabularnewline
6 & 29 & 19.3856186975295 & 9.61438130247047 \tabularnewline
7 & 25 & 18.7430646484596 & 6.25693535154043 \tabularnewline
8 & 25 & 22.6682359987513 & 2.33176400124867 \tabularnewline
9 & 21 & 23.2664418239135 & -2.26644182391349 \tabularnewline
10 & 22 & 23.1694672467477 & -1.16946724674772 \tabularnewline
11 & 22 & 22.0205592022521 & -0.0205592022521363 \tabularnewline
12 & 24 & 25.168403785293 & -1.16840378529302 \tabularnewline
13 & 22 & 23.0158347543062 & -1.01583475430618 \tabularnewline
14 & 23 & 23.6455726019091 & -0.645572601909105 \tabularnewline
15 & 19 & 23.8661543381066 & -4.86615433810661 \tabularnewline
16 & 19 & 24.9880048025088 & -5.98800480250884 \tabularnewline
17 & 21 & 33.2476779414074 & -12.2476779414074 \tabularnewline
18 & 20 & 17.7896071058454 & 2.21039289415457 \tabularnewline
19 & 23 & 20.0615401205848 & 2.9384598794152 \tabularnewline
20 & 11 & 30.949662456511 & -19.949662456511 \tabularnewline
21 & 21 & 20.9868557735693 & 0.0131442264306973 \tabularnewline
22 & 19 & 30.6303012020798 & -11.6303012020798 \tabularnewline
23 & 21 & 20.5194394463632 & 0.480560553636838 \tabularnewline
24 & 23 & 24.2821089082547 & -1.28210890825467 \tabularnewline
25 & 19 & 22.5058912670181 & -3.50589126701808 \tabularnewline
26 & 22 & 22.2911156602836 & -0.291115660283565 \tabularnewline
27 & 19 & 24.4432479625961 & -5.44324796259614 \tabularnewline
28 & 23 & 23.9484168031117 & -0.948416803111689 \tabularnewline
29 & 29 & 29.2223051403605 & -0.222305140360478 \tabularnewline
30 & 27 & 43.0758625085064 & -16.0758625085064 \tabularnewline
31 & 52 & 37.4504557023255 & 14.5495442976745 \tabularnewline
32 & 46 & 38.6743437547082 & 7.3256562452918 \tabularnewline
33 & 58 & 39.6017002576971 & 18.3982997423029 \tabularnewline
34 & 54 & 39.1627375515758 & 14.8372624484242 \tabularnewline
35 & 29 & 42.8759554095259 & -13.8759554095259 \tabularnewline
36 & 50 & 41.8598051247369 & 8.14019487526314 \tabularnewline
37 & 43 & 40.6591807710708 & 2.34081922892917 \tabularnewline
38 & 30 & 39.7990950120652 & -9.79909501206515 \tabularnewline
39 & 47 & 38.1548661818176 & 8.84513381818242 \tabularnewline
40 & 45 & 32.3920760948634 & 12.6079239051366 \tabularnewline
41 & 1 & 25.4439476935796 & -24.4439476935796 \tabularnewline
42 & 3 & 29.1260721031755 & -26.1260721031755 \tabularnewline
43 & 4 & 24.9159080638744 & -20.9159080638744 \tabularnewline
44 & 5 & 43.4070457103042 & -38.4070457103042 \tabularnewline
45 & 35 & 29.2244787843085 & 5.7755212156915 \tabularnewline
46 & 35 & 33.9112760055802 & 1.08872399441979 \tabularnewline
47 & 21 & 27.5185657905591 & -6.51856579055908 \tabularnewline
48 & 33 & 27.9052111152754 & 5.09478888472456 \tabularnewline
49 & 40 & 27.6115848777829 & 12.3884151222171 \tabularnewline
50 & 22 & 25.2877791951336 & -3.28777919513363 \tabularnewline
51 & 35 & 31.297163761819 & 3.70283623818099 \tabularnewline
52 & 20 & 23.6882451211629 & -3.68824512116287 \tabularnewline
53 & 28 & 31.2894928563588 & -3.28949285635877 \tabularnewline
54 & 46 & 36.2957084197491 & 9.70429158025093 \tabularnewline
55 & 18 & 29.0530876863671 & -11.0530876863671 \tabularnewline
56 & 22 & 28.5516091100055 & -6.55160911000551 \tabularnewline
57 & 20 & 22.1573180340913 & -2.15731803409132 \tabularnewline
58 & 25 & 28.1543704591459 & -3.15437045914591 \tabularnewline
59 & 31 & 31.5139676779368 & -0.513967677936793 \tabularnewline
60 & 21 & 25.9910725730771 & -4.99107257307712 \tabularnewline
61 & 23 & 25.3729960749065 & -2.3729960749065 \tabularnewline
62 & 26 & 34.1701657726983 & -8.1701657726983 \tabularnewline
63 & 34 & 32.1313912291573 & 1.8686087708427 \tabularnewline
64 & 31 & 32.7573745048708 & -1.75737450487082 \tabularnewline
65 & 23 & 24.5593903594388 & -1.55939035943877 \tabularnewline
66 & 31 & 25.5075150757564 & 5.49248492424363 \tabularnewline
67 & 26 & 26.3505567828462 & -0.350556782846201 \tabularnewline
68 & 36 & 27.7415338862523 & 8.2584661137477 \tabularnewline
69 & 28 & 31.4066900418439 & -3.40669004184395 \tabularnewline
70 & 34 & 26.7751441404037 & 7.22485585959629 \tabularnewline
71 & 25 & 29.8403298621303 & -4.8403298621303 \tabularnewline
72 & 33 & 36.4018598303324 & -3.40185983033239 \tabularnewline
73 & 46 & 34.411648096307 & 11.588351903693 \tabularnewline
74 & 24 & 30.8806902146584 & -6.8806902146584 \tabularnewline
75 & 32 & 34.240882116718 & -2.24088211671803 \tabularnewline
76 & 33 & 24.8457235853604 & 8.15427641463962 \tabularnewline
77 & 42 & 36.7499372059277 & 5.2500627940723 \tabularnewline
78 & 17 & 24.6725863811359 & -7.67258638113595 \tabularnewline
79 & 36 & 28.4048382173346 & 7.5951617826654 \tabularnewline
80 & 40 & 32.6957231333812 & 7.30427686661876 \tabularnewline
81 & 30 & 33.8707904565763 & -3.87079045657634 \tabularnewline
82 & 19 & 39.1403382599544 & -20.1403382599544 \tabularnewline
83 & 33 & 28.7312274136253 & 4.26877258637471 \tabularnewline
84 & 35 & 26.2421932782159 & 8.75780672178407 \tabularnewline
85 & 23 & 28.044172588995 & -5.04417258899498 \tabularnewline
86 & 15 & 23.5007379467095 & -8.50073794670946 \tabularnewline
87 & 38 & 37.7319902629241 & 0.268009737075917 \tabularnewline
88 & 37 & 28.5978346382849 & 8.40216536171506 \tabularnewline
89 & 23 & 26.3483891866514 & -3.34838918665136 \tabularnewline
90 & 41 & 32.4038973180102 & 8.59610268198982 \tabularnewline
91 & 34 & 33.0626952372673 & 0.937304762732664 \tabularnewline
92 & 38 & 30.7097484352265 & 7.29025156477346 \tabularnewline
93 & 45 & 29.8865534164882 & 15.1134465835118 \tabularnewline
94 & 27 & 25.5757244534985 & 1.42427554650147 \tabularnewline
95 & 46 & 36.0046569314304 & 9.99534306856957 \tabularnewline
96 & 26 & 29.7575110723 & -3.75751107230004 \tabularnewline
97 & 44 & 35.7090487155723 & 8.29095128442775 \tabularnewline
98 & 36 & 27.8633307012742 & 8.13666929872585 \tabularnewline
99 & 20 & 31.5289118550932 & -11.5289118550932 \tabularnewline
100 & 44 & 32.2596022200666 & 11.7403977799334 \tabularnewline
101 & 27 & 34.1718421836245 & -7.17184218362446 \tabularnewline
102 & 27 & 29.5732814086967 & -2.57328140869672 \tabularnewline
103 & 41 & 32.6650299923254 & 8.33497000767465 \tabularnewline
104 & 30 & 28.7033597545219 & 1.29664024547813 \tabularnewline
105 & 33 & 34.3596217800457 & -1.35962178004566 \tabularnewline
106 & 37 & 26.9645385411172 & 10.0354614588828 \tabularnewline
107 & 30 & 30.6180941559829 & -0.618094155982933 \tabularnewline
108 & 20 & 24.2016441672297 & -4.20164416722974 \tabularnewline
109 & 44 & 37.3874083635522 & 6.61259163644775 \tabularnewline
110 & 20 & 24.7570084575151 & -4.75700845751505 \tabularnewline
111 & 33 & 28.3388367927594 & 4.66116320724062 \tabularnewline
112 & 31 & 36.1185594972838 & -5.11855949728375 \tabularnewline
113 & 23 & 32.0759972549547 & -9.07599725495468 \tabularnewline
114 & 33 & 31.7216259321873 & 1.27837406781273 \tabularnewline
115 & 33 & 25.7252450972587 & 7.27475490274133 \tabularnewline
116 & 32 & 28.0191136811288 & 3.98088631887124 \tabularnewline
117 & 25 & 27.8388646312644 & -2.8388646312644 \tabularnewline
118 & 37 & 42.8425156230228 & -5.84251562302283 \tabularnewline
119 & 48 & 39.1393976824685 & 8.86060231753151 \tabularnewline
120 & 45 & 38.4590446944559 & 6.5409553055441 \tabularnewline
121 & 32 & 41.1596830916966 & -9.15968309169657 \tabularnewline
122 & 46 & 41.4326636351029 & 4.56733636489714 \tabularnewline
123 & 20 & 42.658514607755 & -22.658514607755 \tabularnewline
124 & 42 & 37.2210525394832 & 4.77894746051684 \tabularnewline
125 & 45 & 30.8312465888785 & 14.1687534111215 \tabularnewline
126 & 29 & 38.7523182355638 & -9.75231823556379 \tabularnewline
127 & 51 & 42.1721179609673 & 8.82788203903274 \tabularnewline
128 & 55 & 38.5806547771155 & 16.4193452228845 \tabularnewline
129 & 50 & 40.3542386690303 & 9.64576133096973 \tabularnewline
130 & 44 & 40.1964049541366 & 3.80359504586339 \tabularnewline
131 & 41 & 34.5084929360706 & 6.4915070639294 \tabularnewline
132 & 40 & 37.9865421328518 & 2.01345786714818 \tabularnewline
133 & 47 & 38.0826316980718 & 8.91736830192823 \tabularnewline
134 & 42 & 34.9518265865393 & 7.04817341346067 \tabularnewline
135 & 40 & 38.0837237674786 & 1.91627623252136 \tabularnewline
136 & 51 & 40.5224501630833 & 10.4775498369167 \tabularnewline
137 & 43 & 40.8654119166818 & 2.13458808331817 \tabularnewline
138 & 45 & 39.3843979448103 & 5.61560205518968 \tabularnewline
139 & 41 & 32.8107170009084 & 8.1892829990916 \tabularnewline
140 & 41 & 38.1256927573231 & 2.87430724267686 \tabularnewline
141 & 37 & 39.8478435187051 & -2.84784351870506 \tabularnewline
142 & 46 & 34.6892134190821 & 11.3107865809179 \tabularnewline
143 & 38 & 38.7857446711579 & -0.785744671157946 \tabularnewline
144 & 39 & 32.5768279394361 & 6.42317206056392 \tabularnewline
145 & 45 & 28.1156710501322 & 16.8843289498678 \tabularnewline
146 & 28 & 26.6180752684827 & 1.38192473151731 \tabularnewline
147 & 45 & 37.1591680253186 & 7.84083197468137 \tabularnewline
148 & 21 & 27.1314689730685 & -6.13146897306851 \tabularnewline
149 & 33 & 35.8654777966963 & -2.8654777966963 \tabularnewline
150 & 24 & 21.3875891395787 & 2.61241086042127 \tabularnewline
151 & 16 & 21.267268615358 & -5.26726861535805 \tabularnewline
152 & 23 & 42.383847373134 & -19.383847373134 \tabularnewline
153 & 40 & 39.7850955834691 & 0.214904416530853 \tabularnewline
154 & 49 & 41.538707661304 & 7.46129233869598 \tabularnewline
155 & 38 & 42.2356109094779 & -4.23561090947787 \tabularnewline
156 & 32 & 39.4641123378523 & -7.46411233785233 \tabularnewline
157 & 46 & 39.7590443831771 & 6.24095561682288 \tabularnewline
158 & 32 & 41.8293045142391 & -9.82930451423912 \tabularnewline
159 & 41 & 43.8411207733459 & -2.84112077334594 \tabularnewline
160 & 43 & 42.7722082651476 & 0.227791734852425 \tabularnewline
161 & 44 & 41.9629921618101 & 2.0370078381899 \tabularnewline
162 & 47 & 39.956952012003 & 7.04304798799703 \tabularnewline
163 & 28 & 42.1311359098207 & -14.1311359098207 \tabularnewline
164 & 52 & 41.9411825777961 & 10.0588174222039 \tabularnewline
165 & 27 & 41.1011942399206 & -14.1011942399206 \tabularnewline
166 & 45 & 41.6360493756894 & 3.36395062431065 \tabularnewline
167 & 27 & 37.2457898800738 & -10.2457898800737 \tabularnewline
168 & 25 & 38.4103544268903 & -13.4103544268903 \tabularnewline
169 & 28 & 40.0364179431271 & -12.0364179431271 \tabularnewline
170 & 25 & 43.9528100691583 & -18.9528100691583 \tabularnewline
171 & 52 & 39.5883616994112 & 12.4116383005888 \tabularnewline
172 & 44 & 43.5772562546508 & 0.422743745349184 \tabularnewline
173 & 43 & 39.0746000596783 & 3.92539994032169 \tabularnewline
174 & 47 & 40.9183901930039 & 6.08160980699606 \tabularnewline
175 & 52 & 40.2724380877629 & 11.7275619122371 \tabularnewline
176 & 40 & 40.626081177185 & -0.626081177185027 \tabularnewline
177 & 42 & 40.1577977447423 & 1.84220225525766 \tabularnewline
178 & 45 & 40.0484286213844 & 4.95157137861561 \tabularnewline
179 & 45 & 45.6646677181087 & -0.664667718108656 \tabularnewline
180 & 50 & 40.1240311411379 & 9.87596885886205 \tabularnewline
181 & 49 & 41.6616763756815 & 7.33832362431851 \tabularnewline
182 & 52 & 38.3584189763525 & 13.6415810236475 \tabularnewline
183 & 48 & 38.4470047146676 & 9.55299528533236 \tabularnewline
184 & 51 & 37.1518599262278 & 13.8481400737722 \tabularnewline
185 & 49 & 41.7266980362053 & 7.27330196379472 \tabularnewline
186 & 31 & 41.1435026367017 & -10.1435026367017 \tabularnewline
187 & 43 & 38.7709424119061 & 4.22905758809391 \tabularnewline
188 & 31 & 38.8929230361987 & -7.89292303619866 \tabularnewline
189 & 28 & 39.4151245301093 & -11.4151245301093 \tabularnewline
190 & 43 & 42.2866993284469 & 0.713300671553054 \tabularnewline
191 & 31 & 43.214605218925 & -12.214605218925 \tabularnewline
192 & 51 & 43.0043201526888 & 7.99567984731121 \tabularnewline
193 & 58 & 39.8029398171137 & 18.1970601828863 \tabularnewline
194 & 25 & 37.448188012544 & -12.448188012544 \tabularnewline
195 & 27 & 38.5622024299982 & -11.5622024299982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]22[/C][C]25.117503719148[/C][C]-3.11750371914802[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]23.8429415262079[/C][C]-0.842941526207933[/C][/ROW]
[ROW][C]3[/C][C]27[/C][C]30.9245609576659[/C][C]-3.92456095766588[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]29.4370628035319[/C][C]-10.4370628035319[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]32.1968402276497[/C][C]-17.1968402276497[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]19.3856186975295[/C][C]9.61438130247047[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]18.7430646484596[/C][C]6.25693535154043[/C][/ROW]
[ROW][C]8[/C][C]25[/C][C]22.6682359987513[/C][C]2.33176400124867[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]23.2664418239135[/C][C]-2.26644182391349[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]23.1694672467477[/C][C]-1.16946724674772[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]22.0205592022521[/C][C]-0.0205592022521363[/C][/ROW]
[ROW][C]12[/C][C]24[/C][C]25.168403785293[/C][C]-1.16840378529302[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]23.0158347543062[/C][C]-1.01583475430618[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]23.6455726019091[/C][C]-0.645572601909105[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]23.8661543381066[/C][C]-4.86615433810661[/C][/ROW]
[ROW][C]16[/C][C]19[/C][C]24.9880048025088[/C][C]-5.98800480250884[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]33.2476779414074[/C][C]-12.2476779414074[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]17.7896071058454[/C][C]2.21039289415457[/C][/ROW]
[ROW][C]19[/C][C]23[/C][C]20.0615401205848[/C][C]2.9384598794152[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]30.949662456511[/C][C]-19.949662456511[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]20.9868557735693[/C][C]0.0131442264306973[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]30.6303012020798[/C][C]-11.6303012020798[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.5194394463632[/C][C]0.480560553636838[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]24.2821089082547[/C][C]-1.28210890825467[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]22.5058912670181[/C][C]-3.50589126701808[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]22.2911156602836[/C][C]-0.291115660283565[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]24.4432479625961[/C][C]-5.44324796259614[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]23.9484168031117[/C][C]-0.948416803111689[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]29.2223051403605[/C][C]-0.222305140360478[/C][/ROW]
[ROW][C]30[/C][C]27[/C][C]43.0758625085064[/C][C]-16.0758625085064[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]37.4504557023255[/C][C]14.5495442976745[/C][/ROW]
[ROW][C]32[/C][C]46[/C][C]38.6743437547082[/C][C]7.3256562452918[/C][/ROW]
[ROW][C]33[/C][C]58[/C][C]39.6017002576971[/C][C]18.3982997423029[/C][/ROW]
[ROW][C]34[/C][C]54[/C][C]39.1627375515758[/C][C]14.8372624484242[/C][/ROW]
[ROW][C]35[/C][C]29[/C][C]42.8759554095259[/C][C]-13.8759554095259[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]41.8598051247369[/C][C]8.14019487526314[/C][/ROW]
[ROW][C]37[/C][C]43[/C][C]40.6591807710708[/C][C]2.34081922892917[/C][/ROW]
[ROW][C]38[/C][C]30[/C][C]39.7990950120652[/C][C]-9.79909501206515[/C][/ROW]
[ROW][C]39[/C][C]47[/C][C]38.1548661818176[/C][C]8.84513381818242[/C][/ROW]
[ROW][C]40[/C][C]45[/C][C]32.3920760948634[/C][C]12.6079239051366[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]25.4439476935796[/C][C]-24.4439476935796[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]29.1260721031755[/C][C]-26.1260721031755[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]24.9159080638744[/C][C]-20.9159080638744[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]43.4070457103042[/C][C]-38.4070457103042[/C][/ROW]
[ROW][C]45[/C][C]35[/C][C]29.2244787843085[/C][C]5.7755212156915[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]33.9112760055802[/C][C]1.08872399441979[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]27.5185657905591[/C][C]-6.51856579055908[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]27.9052111152754[/C][C]5.09478888472456[/C][/ROW]
[ROW][C]49[/C][C]40[/C][C]27.6115848777829[/C][C]12.3884151222171[/C][/ROW]
[ROW][C]50[/C][C]22[/C][C]25.2877791951336[/C][C]-3.28777919513363[/C][/ROW]
[ROW][C]51[/C][C]35[/C][C]31.297163761819[/C][C]3.70283623818099[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]23.6882451211629[/C][C]-3.68824512116287[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]31.2894928563588[/C][C]-3.28949285635877[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]36.2957084197491[/C][C]9.70429158025093[/C][/ROW]
[ROW][C]55[/C][C]18[/C][C]29.0530876863671[/C][C]-11.0530876863671[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]28.5516091100055[/C][C]-6.55160911000551[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]22.1573180340913[/C][C]-2.15731803409132[/C][/ROW]
[ROW][C]58[/C][C]25[/C][C]28.1543704591459[/C][C]-3.15437045914591[/C][/ROW]
[ROW][C]59[/C][C]31[/C][C]31.5139676779368[/C][C]-0.513967677936793[/C][/ROW]
[ROW][C]60[/C][C]21[/C][C]25.9910725730771[/C][C]-4.99107257307712[/C][/ROW]
[ROW][C]61[/C][C]23[/C][C]25.3729960749065[/C][C]-2.3729960749065[/C][/ROW]
[ROW][C]62[/C][C]26[/C][C]34.1701657726983[/C][C]-8.1701657726983[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]32.1313912291573[/C][C]1.8686087708427[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]32.7573745048708[/C][C]-1.75737450487082[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]24.5593903594388[/C][C]-1.55939035943877[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]25.5075150757564[/C][C]5.49248492424363[/C][/ROW]
[ROW][C]67[/C][C]26[/C][C]26.3505567828462[/C][C]-0.350556782846201[/C][/ROW]
[ROW][C]68[/C][C]36[/C][C]27.7415338862523[/C][C]8.2584661137477[/C][/ROW]
[ROW][C]69[/C][C]28[/C][C]31.4066900418439[/C][C]-3.40669004184395[/C][/ROW]
[ROW][C]70[/C][C]34[/C][C]26.7751441404037[/C][C]7.22485585959629[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]29.8403298621303[/C][C]-4.8403298621303[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]36.4018598303324[/C][C]-3.40185983033239[/C][/ROW]
[ROW][C]73[/C][C]46[/C][C]34.411648096307[/C][C]11.588351903693[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]30.8806902146584[/C][C]-6.8806902146584[/C][/ROW]
[ROW][C]75[/C][C]32[/C][C]34.240882116718[/C][C]-2.24088211671803[/C][/ROW]
[ROW][C]76[/C][C]33[/C][C]24.8457235853604[/C][C]8.15427641463962[/C][/ROW]
[ROW][C]77[/C][C]42[/C][C]36.7499372059277[/C][C]5.2500627940723[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]24.6725863811359[/C][C]-7.67258638113595[/C][/ROW]
[ROW][C]79[/C][C]36[/C][C]28.4048382173346[/C][C]7.5951617826654[/C][/ROW]
[ROW][C]80[/C][C]40[/C][C]32.6957231333812[/C][C]7.30427686661876[/C][/ROW]
[ROW][C]81[/C][C]30[/C][C]33.8707904565763[/C][C]-3.87079045657634[/C][/ROW]
[ROW][C]82[/C][C]19[/C][C]39.1403382599544[/C][C]-20.1403382599544[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]28.7312274136253[/C][C]4.26877258637471[/C][/ROW]
[ROW][C]84[/C][C]35[/C][C]26.2421932782159[/C][C]8.75780672178407[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]28.044172588995[/C][C]-5.04417258899498[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]23.5007379467095[/C][C]-8.50073794670946[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]37.7319902629241[/C][C]0.268009737075917[/C][/ROW]
[ROW][C]88[/C][C]37[/C][C]28.5978346382849[/C][C]8.40216536171506[/C][/ROW]
[ROW][C]89[/C][C]23[/C][C]26.3483891866514[/C][C]-3.34838918665136[/C][/ROW]
[ROW][C]90[/C][C]41[/C][C]32.4038973180102[/C][C]8.59610268198982[/C][/ROW]
[ROW][C]91[/C][C]34[/C][C]33.0626952372673[/C][C]0.937304762732664[/C][/ROW]
[ROW][C]92[/C][C]38[/C][C]30.7097484352265[/C][C]7.29025156477346[/C][/ROW]
[ROW][C]93[/C][C]45[/C][C]29.8865534164882[/C][C]15.1134465835118[/C][/ROW]
[ROW][C]94[/C][C]27[/C][C]25.5757244534985[/C][C]1.42427554650147[/C][/ROW]
[ROW][C]95[/C][C]46[/C][C]36.0046569314304[/C][C]9.99534306856957[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]29.7575110723[/C][C]-3.75751107230004[/C][/ROW]
[ROW][C]97[/C][C]44[/C][C]35.7090487155723[/C][C]8.29095128442775[/C][/ROW]
[ROW][C]98[/C][C]36[/C][C]27.8633307012742[/C][C]8.13666929872585[/C][/ROW]
[ROW][C]99[/C][C]20[/C][C]31.5289118550932[/C][C]-11.5289118550932[/C][/ROW]
[ROW][C]100[/C][C]44[/C][C]32.2596022200666[/C][C]11.7403977799334[/C][/ROW]
[ROW][C]101[/C][C]27[/C][C]34.1718421836245[/C][C]-7.17184218362446[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]29.5732814086967[/C][C]-2.57328140869672[/C][/ROW]
[ROW][C]103[/C][C]41[/C][C]32.6650299923254[/C][C]8.33497000767465[/C][/ROW]
[ROW][C]104[/C][C]30[/C][C]28.7033597545219[/C][C]1.29664024547813[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]34.3596217800457[/C][C]-1.35962178004566[/C][/ROW]
[ROW][C]106[/C][C]37[/C][C]26.9645385411172[/C][C]10.0354614588828[/C][/ROW]
[ROW][C]107[/C][C]30[/C][C]30.6180941559829[/C][C]-0.618094155982933[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]24.2016441672297[/C][C]-4.20164416722974[/C][/ROW]
[ROW][C]109[/C][C]44[/C][C]37.3874083635522[/C][C]6.61259163644775[/C][/ROW]
[ROW][C]110[/C][C]20[/C][C]24.7570084575151[/C][C]-4.75700845751505[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]28.3388367927594[/C][C]4.66116320724062[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]36.1185594972838[/C][C]-5.11855949728375[/C][/ROW]
[ROW][C]113[/C][C]23[/C][C]32.0759972549547[/C][C]-9.07599725495468[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]31.7216259321873[/C][C]1.27837406781273[/C][/ROW]
[ROW][C]115[/C][C]33[/C][C]25.7252450972587[/C][C]7.27475490274133[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]28.0191136811288[/C][C]3.98088631887124[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]27.8388646312644[/C][C]-2.8388646312644[/C][/ROW]
[ROW][C]118[/C][C]37[/C][C]42.8425156230228[/C][C]-5.84251562302283[/C][/ROW]
[ROW][C]119[/C][C]48[/C][C]39.1393976824685[/C][C]8.86060231753151[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]38.4590446944559[/C][C]6.5409553055441[/C][/ROW]
[ROW][C]121[/C][C]32[/C][C]41.1596830916966[/C][C]-9.15968309169657[/C][/ROW]
[ROW][C]122[/C][C]46[/C][C]41.4326636351029[/C][C]4.56733636489714[/C][/ROW]
[ROW][C]123[/C][C]20[/C][C]42.658514607755[/C][C]-22.658514607755[/C][/ROW]
[ROW][C]124[/C][C]42[/C][C]37.2210525394832[/C][C]4.77894746051684[/C][/ROW]
[ROW][C]125[/C][C]45[/C][C]30.8312465888785[/C][C]14.1687534111215[/C][/ROW]
[ROW][C]126[/C][C]29[/C][C]38.7523182355638[/C][C]-9.75231823556379[/C][/ROW]
[ROW][C]127[/C][C]51[/C][C]42.1721179609673[/C][C]8.82788203903274[/C][/ROW]
[ROW][C]128[/C][C]55[/C][C]38.5806547771155[/C][C]16.4193452228845[/C][/ROW]
[ROW][C]129[/C][C]50[/C][C]40.3542386690303[/C][C]9.64576133096973[/C][/ROW]
[ROW][C]130[/C][C]44[/C][C]40.1964049541366[/C][C]3.80359504586339[/C][/ROW]
[ROW][C]131[/C][C]41[/C][C]34.5084929360706[/C][C]6.4915070639294[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]37.9865421328518[/C][C]2.01345786714818[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]38.0826316980718[/C][C]8.91736830192823[/C][/ROW]
[ROW][C]134[/C][C]42[/C][C]34.9518265865393[/C][C]7.04817341346067[/C][/ROW]
[ROW][C]135[/C][C]40[/C][C]38.0837237674786[/C][C]1.91627623252136[/C][/ROW]
[ROW][C]136[/C][C]51[/C][C]40.5224501630833[/C][C]10.4775498369167[/C][/ROW]
[ROW][C]137[/C][C]43[/C][C]40.8654119166818[/C][C]2.13458808331817[/C][/ROW]
[ROW][C]138[/C][C]45[/C][C]39.3843979448103[/C][C]5.61560205518968[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]32.8107170009084[/C][C]8.1892829990916[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]38.1256927573231[/C][C]2.87430724267686[/C][/ROW]
[ROW][C]141[/C][C]37[/C][C]39.8478435187051[/C][C]-2.84784351870506[/C][/ROW]
[ROW][C]142[/C][C]46[/C][C]34.6892134190821[/C][C]11.3107865809179[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]38.7857446711579[/C][C]-0.785744671157946[/C][/ROW]
[ROW][C]144[/C][C]39[/C][C]32.5768279394361[/C][C]6.42317206056392[/C][/ROW]
[ROW][C]145[/C][C]45[/C][C]28.1156710501322[/C][C]16.8843289498678[/C][/ROW]
[ROW][C]146[/C][C]28[/C][C]26.6180752684827[/C][C]1.38192473151731[/C][/ROW]
[ROW][C]147[/C][C]45[/C][C]37.1591680253186[/C][C]7.84083197468137[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]27.1314689730685[/C][C]-6.13146897306851[/C][/ROW]
[ROW][C]149[/C][C]33[/C][C]35.8654777966963[/C][C]-2.8654777966963[/C][/ROW]
[ROW][C]150[/C][C]24[/C][C]21.3875891395787[/C][C]2.61241086042127[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]21.267268615358[/C][C]-5.26726861535805[/C][/ROW]
[ROW][C]152[/C][C]23[/C][C]42.383847373134[/C][C]-19.383847373134[/C][/ROW]
[ROW][C]153[/C][C]40[/C][C]39.7850955834691[/C][C]0.214904416530853[/C][/ROW]
[ROW][C]154[/C][C]49[/C][C]41.538707661304[/C][C]7.46129233869598[/C][/ROW]
[ROW][C]155[/C][C]38[/C][C]42.2356109094779[/C][C]-4.23561090947787[/C][/ROW]
[ROW][C]156[/C][C]32[/C][C]39.4641123378523[/C][C]-7.46411233785233[/C][/ROW]
[ROW][C]157[/C][C]46[/C][C]39.7590443831771[/C][C]6.24095561682288[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]41.8293045142391[/C][C]-9.82930451423912[/C][/ROW]
[ROW][C]159[/C][C]41[/C][C]43.8411207733459[/C][C]-2.84112077334594[/C][/ROW]
[ROW][C]160[/C][C]43[/C][C]42.7722082651476[/C][C]0.227791734852425[/C][/ROW]
[ROW][C]161[/C][C]44[/C][C]41.9629921618101[/C][C]2.0370078381899[/C][/ROW]
[ROW][C]162[/C][C]47[/C][C]39.956952012003[/C][C]7.04304798799703[/C][/ROW]
[ROW][C]163[/C][C]28[/C][C]42.1311359098207[/C][C]-14.1311359098207[/C][/ROW]
[ROW][C]164[/C][C]52[/C][C]41.9411825777961[/C][C]10.0588174222039[/C][/ROW]
[ROW][C]165[/C][C]27[/C][C]41.1011942399206[/C][C]-14.1011942399206[/C][/ROW]
[ROW][C]166[/C][C]45[/C][C]41.6360493756894[/C][C]3.36395062431065[/C][/ROW]
[ROW][C]167[/C][C]27[/C][C]37.2457898800738[/C][C]-10.2457898800737[/C][/ROW]
[ROW][C]168[/C][C]25[/C][C]38.4103544268903[/C][C]-13.4103544268903[/C][/ROW]
[ROW][C]169[/C][C]28[/C][C]40.0364179431271[/C][C]-12.0364179431271[/C][/ROW]
[ROW][C]170[/C][C]25[/C][C]43.9528100691583[/C][C]-18.9528100691583[/C][/ROW]
[ROW][C]171[/C][C]52[/C][C]39.5883616994112[/C][C]12.4116383005888[/C][/ROW]
[ROW][C]172[/C][C]44[/C][C]43.5772562546508[/C][C]0.422743745349184[/C][/ROW]
[ROW][C]173[/C][C]43[/C][C]39.0746000596783[/C][C]3.92539994032169[/C][/ROW]
[ROW][C]174[/C][C]47[/C][C]40.9183901930039[/C][C]6.08160980699606[/C][/ROW]
[ROW][C]175[/C][C]52[/C][C]40.2724380877629[/C][C]11.7275619122371[/C][/ROW]
[ROW][C]176[/C][C]40[/C][C]40.626081177185[/C][C]-0.626081177185027[/C][/ROW]
[ROW][C]177[/C][C]42[/C][C]40.1577977447423[/C][C]1.84220225525766[/C][/ROW]
[ROW][C]178[/C][C]45[/C][C]40.0484286213844[/C][C]4.95157137861561[/C][/ROW]
[ROW][C]179[/C][C]45[/C][C]45.6646677181087[/C][C]-0.664667718108656[/C][/ROW]
[ROW][C]180[/C][C]50[/C][C]40.1240311411379[/C][C]9.87596885886205[/C][/ROW]
[ROW][C]181[/C][C]49[/C][C]41.6616763756815[/C][C]7.33832362431851[/C][/ROW]
[ROW][C]182[/C][C]52[/C][C]38.3584189763525[/C][C]13.6415810236475[/C][/ROW]
[ROW][C]183[/C][C]48[/C][C]38.4470047146676[/C][C]9.55299528533236[/C][/ROW]
[ROW][C]184[/C][C]51[/C][C]37.1518599262278[/C][C]13.8481400737722[/C][/ROW]
[ROW][C]185[/C][C]49[/C][C]41.7266980362053[/C][C]7.27330196379472[/C][/ROW]
[ROW][C]186[/C][C]31[/C][C]41.1435026367017[/C][C]-10.1435026367017[/C][/ROW]
[ROW][C]187[/C][C]43[/C][C]38.7709424119061[/C][C]4.22905758809391[/C][/ROW]
[ROW][C]188[/C][C]31[/C][C]38.8929230361987[/C][C]-7.89292303619866[/C][/ROW]
[ROW][C]189[/C][C]28[/C][C]39.4151245301093[/C][C]-11.4151245301093[/C][/ROW]
[ROW][C]190[/C][C]43[/C][C]42.2866993284469[/C][C]0.713300671553054[/C][/ROW]
[ROW][C]191[/C][C]31[/C][C]43.214605218925[/C][C]-12.214605218925[/C][/ROW]
[ROW][C]192[/C][C]51[/C][C]43.0043201526888[/C][C]7.99567984731121[/C][/ROW]
[ROW][C]193[/C][C]58[/C][C]39.8029398171137[/C][C]18.1970601828863[/C][/ROW]
[ROW][C]194[/C][C]25[/C][C]37.448188012544[/C][C]-12.448188012544[/C][/ROW]
[ROW][C]195[/C][C]27[/C][C]38.5622024299982[/C][C]-11.5622024299982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12225.117503719148-3.11750371914802
22323.8429415262079-0.842941526207933
32730.9245609576659-3.92456095766588
41929.4370628035319-10.4370628035319
51532.1968402276497-17.1968402276497
62919.38561869752959.61438130247047
72518.74306464845966.25693535154043
82522.66823599875132.33176400124867
92123.2664418239135-2.26644182391349
102223.1694672467477-1.16946724674772
112222.0205592022521-0.0205592022521363
122425.168403785293-1.16840378529302
132223.0158347543062-1.01583475430618
142323.6455726019091-0.645572601909105
151923.8661543381066-4.86615433810661
161924.9880048025088-5.98800480250884
172133.2476779414074-12.2476779414074
182017.78960710584542.21039289415457
192320.06154012058482.9384598794152
201130.949662456511-19.949662456511
212120.98685577356930.0131442264306973
221930.6303012020798-11.6303012020798
232120.51943944636320.480560553636838
242324.2821089082547-1.28210890825467
251922.5058912670181-3.50589126701808
262222.2911156602836-0.291115660283565
271924.4432479625961-5.44324796259614
282323.9484168031117-0.948416803111689
292929.2223051403605-0.222305140360478
302743.0758625085064-16.0758625085064
315237.450455702325514.5495442976745
324638.67434375470827.3256562452918
335839.601700257697118.3982997423029
345439.162737551575814.8372624484242
352942.8759554095259-13.8759554095259
365041.85980512473698.14019487526314
374340.65918077107082.34081922892917
383039.7990950120652-9.79909501206515
394738.15486618181768.84513381818242
404532.392076094863412.6079239051366
41125.4439476935796-24.4439476935796
42329.1260721031755-26.1260721031755
43424.9159080638744-20.9159080638744
44543.4070457103042-38.4070457103042
453529.22447878430855.7755212156915
463533.91127600558021.08872399441979
472127.5185657905591-6.51856579055908
483327.90521111527545.09478888472456
494027.611584877782912.3884151222171
502225.2877791951336-3.28777919513363
513531.2971637618193.70283623818099
522023.6882451211629-3.68824512116287
532831.2894928563588-3.28949285635877
544636.29570841974919.70429158025093
551829.0530876863671-11.0530876863671
562228.5516091100055-6.55160911000551
572022.1573180340913-2.15731803409132
582528.1543704591459-3.15437045914591
593131.5139676779368-0.513967677936793
602125.9910725730771-4.99107257307712
612325.3729960749065-2.3729960749065
622634.1701657726983-8.1701657726983
633432.13139122915731.8686087708427
643132.7573745048708-1.75737450487082
652324.5593903594388-1.55939035943877
663125.50751507575645.49248492424363
672626.3505567828462-0.350556782846201
683627.74153388625238.2584661137477
692831.4066900418439-3.40669004184395
703426.77514414040377.22485585959629
712529.8403298621303-4.8403298621303
723336.4018598303324-3.40185983033239
734634.41164809630711.588351903693
742430.8806902146584-6.8806902146584
753234.240882116718-2.24088211671803
763324.84572358536048.15427641463962
774236.74993720592775.2500627940723
781724.6725863811359-7.67258638113595
793628.40483821733467.5951617826654
804032.69572313338127.30427686661876
813033.8707904565763-3.87079045657634
821939.1403382599544-20.1403382599544
833328.73122741362534.26877258637471
843526.24219327821598.75780672178407
852328.044172588995-5.04417258899498
861523.5007379467095-8.50073794670946
873837.73199026292410.268009737075917
883728.59783463828498.40216536171506
892326.3483891866514-3.34838918665136
904132.40389731801028.59610268198982
913433.06269523726730.937304762732664
923830.70974843522657.29025156477346
934529.886553416488215.1134465835118
942725.57572445349851.42427554650147
954636.00465693143049.99534306856957
962629.7575110723-3.75751107230004
974435.70904871557238.29095128442775
983627.86333070127428.13666929872585
992031.5289118550932-11.5289118550932
1004432.259602220066611.7403977799334
1012734.1718421836245-7.17184218362446
1022729.5732814086967-2.57328140869672
1034132.66502999232548.33497000767465
1043028.70335975452191.29664024547813
1053334.3596217800457-1.35962178004566
1063726.964538541117210.0354614588828
1073030.6180941559829-0.618094155982933
1082024.2016441672297-4.20164416722974
1094437.38740836355226.61259163644775
1102024.7570084575151-4.75700845751505
1113328.33883679275944.66116320724062
1123136.1185594972838-5.11855949728375
1132332.0759972549547-9.07599725495468
1143331.72162593218731.27837406781273
1153325.72524509725877.27475490274133
1163228.01911368112883.98088631887124
1172527.8388646312644-2.8388646312644
1183742.8425156230228-5.84251562302283
1194839.13939768246858.86060231753151
1204538.45904469445596.5409553055441
1213241.1596830916966-9.15968309169657
1224641.43266363510294.56733636489714
1232042.658514607755-22.658514607755
1244237.22105253948324.77894746051684
1254530.831246588878514.1687534111215
1262938.7523182355638-9.75231823556379
1275142.17211796096738.82788203903274
1285538.580654777115516.4193452228845
1295040.35423866903039.64576133096973
1304440.19640495413663.80359504586339
1314134.50849293607066.4915070639294
1324037.98654213285182.01345786714818
1334738.08263169807188.91736830192823
1344234.95182658653937.04817341346067
1354038.08372376747861.91627623252136
1365140.522450163083310.4775498369167
1374340.86541191668182.13458808331817
1384539.38439794481035.61560205518968
1394132.81071700090848.1892829990916
1404138.12569275732312.87430724267686
1413739.8478435187051-2.84784351870506
1424634.689213419082111.3107865809179
1433838.7857446711579-0.785744671157946
1443932.57682793943616.42317206056392
1454528.115671050132216.8843289498678
1462826.61807526848271.38192473151731
1474537.15916802531867.84083197468137
1482127.1314689730685-6.13146897306851
1493335.8654777966963-2.8654777966963
1502421.38758913957872.61241086042127
1511621.267268615358-5.26726861535805
1522342.383847373134-19.383847373134
1534039.78509558346910.214904416530853
1544941.5387076613047.46129233869598
1553842.2356109094779-4.23561090947787
1563239.4641123378523-7.46411233785233
1574639.75904438317716.24095561682288
1583241.8293045142391-9.82930451423912
1594143.8411207733459-2.84112077334594
1604342.77220826514760.227791734852425
1614441.96299216181012.0370078381899
1624739.9569520120037.04304798799703
1632842.1311359098207-14.1311359098207
1645241.941182577796110.0588174222039
1652741.1011942399206-14.1011942399206
1664541.63604937568943.36395062431065
1672737.2457898800738-10.2457898800737
1682538.4103544268903-13.4103544268903
1692840.0364179431271-12.0364179431271
1702543.9528100691583-18.9528100691583
1715239.588361699411212.4116383005888
1724443.57725625465080.422743745349184
1734339.07460005967833.92539994032169
1744740.91839019300396.08160980699606
1755240.272438087762911.7275619122371
1764040.626081177185-0.626081177185027
1774240.15779774474231.84220225525766
1784540.04842862138444.95157137861561
1794545.6646677181087-0.664667718108656
1805040.12403114113799.87596885886205
1814941.66167637568157.33832362431851
1825238.358418976352513.6415810236475
1834838.44700471466769.55299528533236
1845137.151859926227813.8481400737722
1854941.72669803620537.27330196379472
1863141.1435026367017-10.1435026367017
1874338.77094241190614.22905758809391
1883138.8929230361987-7.89292303619866
1892839.4151245301093-11.4151245301093
1904342.28669932844690.713300671553054
1913143.214605218925-12.214605218925
1925143.00432015268887.99567984731121
1935839.802939817113718.1970601828863
1942537.448188012544-12.448188012544
1952738.5622024299982-11.5622024299982






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.01145892327362450.0229178465472490.988541076726375
100.005580255084646570.01116051016929310.994419744915353
110.003830394951385430.007660789902770860.996169605048615
120.001446881691108910.002893763382217830.99855311830889
130.0004068296906227450.000813659381245490.999593170309377
140.0001108453200228620.0002216906400457240.999889154679977
157.2342061789211e-050.0001446841235784220.99992765793821
161.75447938266853e-053.50895876533707e-050.999982455206173
175.37635136478093e-061.07527027295619e-050.999994623648635
182.89050727484676e-065.78101454969353e-060.999997109492725
191.20016921465062e-062.40033842930125e-060.999998799830785
209.13359193218518e-071.82671838643704e-060.999999086640807
212.61232875895227e-075.22465751790455e-070.999999738767124
222.52252598181451e-075.04505196362902e-070.999999747747402
237.2864035954946e-081.45728071909892e-070.999999927135964
241.84663738701328e-083.69327477402656e-080.999999981533626
255.44503040070167e-091.08900608014033e-080.99999999455497
264.41030311973867e-098.82060623947733e-090.999999995589697
271.95896596257068e-093.91793192514135e-090.999999998041034
285.15437507545988e-101.03087501509198e-090.999999999484563
291.68819821438689e-093.37639642877377e-090.999999998311802
305.9772883698613e-101.19545767397226e-090.999999999402271
310.0001506816645327550.000301363329065510.999849318335467
328.0415756876931e-050.0001608315137538620.999919584243123
330.0003659071099302150.000731814219860430.99963409289007
340.001647329585041550.003294659170083110.998352670414958
350.002663321124423520.005326642248847040.997336678875577
360.002758548276660590.005517096553321170.99724145172334
370.00457007359638220.00914014719276440.995429926403618
380.02445120429856580.04890240859713170.975548795701434
390.01834191306157640.03668382612315280.981658086938424
400.01465260367781140.02930520735562280.985347396322189
410.04461983549027160.08923967098054330.955380164509728
420.1543526594979810.3087053189959630.845647340502019
430.1944954064896870.3889908129793740.805504593510313
440.3794246752763560.7588493505527120.620575324723644
450.3365929552365310.6731859104730630.663407044763469
460.780238381263080.439523237473840.21976161873692
470.7705109611154850.458978077769030.229489038884515
480.7431219071644850.513756185671030.256878092835515
490.842645505336920.314708989326160.15735449466308
500.8157055038195190.3685889923609620.184294496180481
510.7956943690302860.4086112619394290.204305630969714
520.7744545300198640.4510909399602730.225545469980136
530.7386582097333680.5226835805332640.261341790266632
540.8652085778887790.2695828442224430.134791422111221
550.855211145068370.2895777098632580.144788854931629
560.8560807565885070.2878384868229850.143919243411493
570.8443511105909670.3112977788180660.155648889409033
580.8185754280991690.3628491438016620.181424571900831
590.7913918252118410.4172163495763180.208608174788159
600.7676274514122940.4647450971754120.232372548587706
610.763725584282320.4725488314353610.23627441571768
620.7607276491621350.4785447016757290.239272350837865
630.7926213191611830.4147573616776340.207378680838817
640.7854981822013880.4290036355972240.214501817798612
650.758863852834670.4822722943306590.24113614716533
660.729698522748910.540602954502180.27030147725109
670.6976887222051810.6046225555896380.302311277794819
680.670509169727910.6589816605441810.329490830272091
690.6340033364609590.7319933270780820.365996663539041
700.5992892304148640.8014215391702710.400710769585136
710.5652256227820440.8695487544359120.434774377217956
720.5259812483444820.9480375033110360.474018751655518
730.5571203989054770.8857592021890460.442879601094523
740.5310714331946770.9378571336106470.468928566805323
750.4967245435716770.9934490871433540.503275456428323
760.4975102186618560.9950204373237130.502489781338144
770.5026291752231350.994741649553730.497370824776865
780.5064257999858330.9871484000283350.493574200014167
790.5345128147011310.9309743705977370.465487185298869
800.5973395850832520.8053208298334950.402660414916748
810.5679743958868760.8640512082262470.432025604113124
820.6602304954978430.6795390090043150.339769504502157
830.6423772413587030.7152455172825930.357622758641297
840.6221470619980020.7557058760039970.377852938001998
850.6119803703959520.7760392592080960.388019629604048
860.6288448101885920.7423103796228170.371155189811408
870.6031370179279380.7937259641441240.396862982072062
880.5820966521661660.8358066956676690.417903347833834
890.5586798885165470.8826402229669050.441320111483453
900.5981570962200260.8036858075599490.401842903779974
910.5703241980178430.8593516039643130.429675801982157
920.5482742532700910.9034514934598180.451725746729909
930.5937088658824080.8125822682351850.406291134117592
940.5526818640052980.8946362719894030.447318135994702
950.5989642361095760.8020715277808490.401035763890424
960.5788046377310240.8423907245379510.421195362268976
970.5971376621099160.8057246757801670.402862337890084
980.5757269424536070.8485461150927850.424273057546393
990.5851420030085310.8297159939829380.414857996991469
1000.6366055336629430.7267889326741130.363394466337057
1010.61504451181320.7699109763735990.384955488186799
1020.5818110708045920.8363778583908160.418188929195408
1030.5682678482017770.8634643035964450.431732151798223
1040.5267289341565010.9465421316869970.473271065843499
1050.4890101526964470.9780203053928940.510989847303553
1060.4840850905159230.9681701810318470.515914909484077
1070.4432511969017210.8865023938034430.556748803098279
1080.4231130511259580.8462261022519150.576886948874042
1090.4207056633130170.8414113266260330.579294336686983
1100.3978120416231970.7956240832463940.602187958376803
1110.3644817518685330.7289635037370660.635518248131467
1120.3313349396436040.6626698792872090.668665060356396
1130.3452325100207820.6904650200415650.654767489979218
1140.3072225446284480.6144450892568960.692777455371552
1150.2850532358517740.5701064717035490.714946764148226
1160.256779663980570.513559327961140.74322033601943
1170.2335237746186240.4670475492372470.766476225381376
1180.2177409541022760.4354819082045530.782259045897724
1190.2691910251967240.5383820503934490.730808974803276
1200.2775299358784830.5550598717569660.722470064121517
1210.2773208787828920.5546417575657830.722679121217108
1220.2639758976801450.527951795360290.736024102319855
1230.4644935868123710.9289871736247430.535506413187629
1240.4556162528207080.9112325056414150.544383747179292
1250.5266866220320220.9466267559359570.473313377967978
1260.5555563597366520.8888872805266970.444443640263349
1270.5483862502290470.9032274995419070.451613749770953
1280.6390285959796820.7219428080406350.360971404020318
1290.6331501455055310.7336997089889370.366849854494469
1300.5993304172637790.8013391654724420.400669582736221
1310.571528528041580.856942943916840.42847147195842
1320.528828213390490.942343573219020.47117178660951
1330.5100020592937680.9799958814124650.489997940706232
1340.4781713570186990.9563427140373970.521828642981301
1350.4409046013920020.8818092027840040.559095398607998
1360.4433797600969560.8867595201939120.556620239903044
1370.3990746657787180.7981493315574360.600925334221282
1380.3654779510367070.7309559020734130.634522048963293
1390.3474507874875110.6949015749750210.65254921251249
1400.3073774481161690.6147548962323390.69262255188383
1410.2716633034827370.5433266069654730.728336696517264
1420.2852265038075520.5704530076151040.714773496192448
1430.249708287811330.4994165756226590.75029171218867
1440.2652288173313140.5304576346626280.734771182668686
1450.4118952230488010.8237904460976020.588104776951199
1460.3811166337940840.7622332675881680.618883366205916
1470.3997748748402530.7995497496805070.600225125159747
1480.3590553327199950.718110665439990.640944667280005
1490.3150504962313180.6301009924626350.684949503768682
1500.2734384535569030.5468769071138070.726561546443097
1510.2372028669615410.4744057339230830.762797133038459
1520.3962798011294880.7925596022589750.603720198870512
1530.3468595014757790.6937190029515570.653140498524222
1540.3160260606152080.6320521212304170.683973939384792
1550.272692380428690.545384760857380.72730761957131
1560.2379211878790640.4758423757581290.762078812120936
1570.2408579872699450.481715974539890.759142012730055
1580.2372864158285470.4745728316570930.762713584171453
1590.1988108996124740.3976217992249490.801189100387526
1600.1627581812124260.3255163624248510.837241818787574
1610.1370372142810660.2740744285621320.862962785718934
1620.1154269284110250.2308538568220510.884573071588975
1630.1217476227680380.2434952455360750.878252377231962
1640.1047490751731660.2094981503463330.895250924826834
1650.1257839601781310.2515679203562620.874216039821869
1660.1140611495062330.2281222990124670.885938850493767
1670.1649481814534910.3298963629069830.835051818546509
1680.2030215628754220.4060431257508440.796978437124578
1690.1955942832168850.3911885664337710.804405716783115
1700.3019372081604860.6038744163209720.698062791839514
1710.3189872460807770.6379744921615530.681012753919223
1720.2590627623262130.5181255246524250.740937237673787
1730.2072318528499310.4144637056998620.792768147150069
1740.1814383989223170.3628767978446330.818561601077683
1750.1857026871275360.3714053742550730.814297312872464
1760.1563470849791670.3126941699583330.843652915020833
1770.1160174150441170.2320348300882340.883982584955883
1780.1020605940961490.2041211881922980.897939405903851
1790.09017416802892910.1803483360578580.90982583197107
1800.1351662217223720.2703324434447440.864833778277628
1810.1115344902226880.2230689804453760.888465509777312
1820.07726311056732120.1545262211346420.922736889432679
1830.05402914401000270.1080582880200050.945970855989997
1840.05125554798097490.102511095961950.948744452019025
1850.08654840045558140.1730968009111630.913451599544419
1860.04484173696538010.08968347393076020.95515826303462

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0114589232736245 & 0.022917846547249 & 0.988541076726375 \tabularnewline
10 & 0.00558025508464657 & 0.0111605101692931 & 0.994419744915353 \tabularnewline
11 & 0.00383039495138543 & 0.00766078990277086 & 0.996169605048615 \tabularnewline
12 & 0.00144688169110891 & 0.00289376338221783 & 0.99855311830889 \tabularnewline
13 & 0.000406829690622745 & 0.00081365938124549 & 0.999593170309377 \tabularnewline
14 & 0.000110845320022862 & 0.000221690640045724 & 0.999889154679977 \tabularnewline
15 & 7.2342061789211e-05 & 0.000144684123578422 & 0.99992765793821 \tabularnewline
16 & 1.75447938266853e-05 & 3.50895876533707e-05 & 0.999982455206173 \tabularnewline
17 & 5.37635136478093e-06 & 1.07527027295619e-05 & 0.999994623648635 \tabularnewline
18 & 2.89050727484676e-06 & 5.78101454969353e-06 & 0.999997109492725 \tabularnewline
19 & 1.20016921465062e-06 & 2.40033842930125e-06 & 0.999998799830785 \tabularnewline
20 & 9.13359193218518e-07 & 1.82671838643704e-06 & 0.999999086640807 \tabularnewline
21 & 2.61232875895227e-07 & 5.22465751790455e-07 & 0.999999738767124 \tabularnewline
22 & 2.52252598181451e-07 & 5.04505196362902e-07 & 0.999999747747402 \tabularnewline
23 & 7.2864035954946e-08 & 1.45728071909892e-07 & 0.999999927135964 \tabularnewline
24 & 1.84663738701328e-08 & 3.69327477402656e-08 & 0.999999981533626 \tabularnewline
25 & 5.44503040070167e-09 & 1.08900608014033e-08 & 0.99999999455497 \tabularnewline
26 & 4.41030311973867e-09 & 8.82060623947733e-09 & 0.999999995589697 \tabularnewline
27 & 1.95896596257068e-09 & 3.91793192514135e-09 & 0.999999998041034 \tabularnewline
28 & 5.15437507545988e-10 & 1.03087501509198e-09 & 0.999999999484563 \tabularnewline
29 & 1.68819821438689e-09 & 3.37639642877377e-09 & 0.999999998311802 \tabularnewline
30 & 5.9772883698613e-10 & 1.19545767397226e-09 & 0.999999999402271 \tabularnewline
31 & 0.000150681664532755 & 0.00030136332906551 & 0.999849318335467 \tabularnewline
32 & 8.0415756876931e-05 & 0.000160831513753862 & 0.999919584243123 \tabularnewline
33 & 0.000365907109930215 & 0.00073181421986043 & 0.99963409289007 \tabularnewline
34 & 0.00164732958504155 & 0.00329465917008311 & 0.998352670414958 \tabularnewline
35 & 0.00266332112442352 & 0.00532664224884704 & 0.997336678875577 \tabularnewline
36 & 0.00275854827666059 & 0.00551709655332117 & 0.99724145172334 \tabularnewline
37 & 0.0045700735963822 & 0.0091401471927644 & 0.995429926403618 \tabularnewline
38 & 0.0244512042985658 & 0.0489024085971317 & 0.975548795701434 \tabularnewline
39 & 0.0183419130615764 & 0.0366838261231528 & 0.981658086938424 \tabularnewline
40 & 0.0146526036778114 & 0.0293052073556228 & 0.985347396322189 \tabularnewline
41 & 0.0446198354902716 & 0.0892396709805433 & 0.955380164509728 \tabularnewline
42 & 0.154352659497981 & 0.308705318995963 & 0.845647340502019 \tabularnewline
43 & 0.194495406489687 & 0.388990812979374 & 0.805504593510313 \tabularnewline
44 & 0.379424675276356 & 0.758849350552712 & 0.620575324723644 \tabularnewline
45 & 0.336592955236531 & 0.673185910473063 & 0.663407044763469 \tabularnewline
46 & 0.78023838126308 & 0.43952323747384 & 0.21976161873692 \tabularnewline
47 & 0.770510961115485 & 0.45897807776903 & 0.229489038884515 \tabularnewline
48 & 0.743121907164485 & 0.51375618567103 & 0.256878092835515 \tabularnewline
49 & 0.84264550533692 & 0.31470898932616 & 0.15735449466308 \tabularnewline
50 & 0.815705503819519 & 0.368588992360962 & 0.184294496180481 \tabularnewline
51 & 0.795694369030286 & 0.408611261939429 & 0.204305630969714 \tabularnewline
52 & 0.774454530019864 & 0.451090939960273 & 0.225545469980136 \tabularnewline
53 & 0.738658209733368 & 0.522683580533264 & 0.261341790266632 \tabularnewline
54 & 0.865208577888779 & 0.269582844222443 & 0.134791422111221 \tabularnewline
55 & 0.85521114506837 & 0.289577709863258 & 0.144788854931629 \tabularnewline
56 & 0.856080756588507 & 0.287838486822985 & 0.143919243411493 \tabularnewline
57 & 0.844351110590967 & 0.311297778818066 & 0.155648889409033 \tabularnewline
58 & 0.818575428099169 & 0.362849143801662 & 0.181424571900831 \tabularnewline
59 & 0.791391825211841 & 0.417216349576318 & 0.208608174788159 \tabularnewline
60 & 0.767627451412294 & 0.464745097175412 & 0.232372548587706 \tabularnewline
61 & 0.76372558428232 & 0.472548831435361 & 0.23627441571768 \tabularnewline
62 & 0.760727649162135 & 0.478544701675729 & 0.239272350837865 \tabularnewline
63 & 0.792621319161183 & 0.414757361677634 & 0.207378680838817 \tabularnewline
64 & 0.785498182201388 & 0.429003635597224 & 0.214501817798612 \tabularnewline
65 & 0.75886385283467 & 0.482272294330659 & 0.24113614716533 \tabularnewline
66 & 0.72969852274891 & 0.54060295450218 & 0.27030147725109 \tabularnewline
67 & 0.697688722205181 & 0.604622555589638 & 0.302311277794819 \tabularnewline
68 & 0.67050916972791 & 0.658981660544181 & 0.329490830272091 \tabularnewline
69 & 0.634003336460959 & 0.731993327078082 & 0.365996663539041 \tabularnewline
70 & 0.599289230414864 & 0.801421539170271 & 0.400710769585136 \tabularnewline
71 & 0.565225622782044 & 0.869548754435912 & 0.434774377217956 \tabularnewline
72 & 0.525981248344482 & 0.948037503311036 & 0.474018751655518 \tabularnewline
73 & 0.557120398905477 & 0.885759202189046 & 0.442879601094523 \tabularnewline
74 & 0.531071433194677 & 0.937857133610647 & 0.468928566805323 \tabularnewline
75 & 0.496724543571677 & 0.993449087143354 & 0.503275456428323 \tabularnewline
76 & 0.497510218661856 & 0.995020437323713 & 0.502489781338144 \tabularnewline
77 & 0.502629175223135 & 0.99474164955373 & 0.497370824776865 \tabularnewline
78 & 0.506425799985833 & 0.987148400028335 & 0.493574200014167 \tabularnewline
79 & 0.534512814701131 & 0.930974370597737 & 0.465487185298869 \tabularnewline
80 & 0.597339585083252 & 0.805320829833495 & 0.402660414916748 \tabularnewline
81 & 0.567974395886876 & 0.864051208226247 & 0.432025604113124 \tabularnewline
82 & 0.660230495497843 & 0.679539009004315 & 0.339769504502157 \tabularnewline
83 & 0.642377241358703 & 0.715245517282593 & 0.357622758641297 \tabularnewline
84 & 0.622147061998002 & 0.755705876003997 & 0.377852938001998 \tabularnewline
85 & 0.611980370395952 & 0.776039259208096 & 0.388019629604048 \tabularnewline
86 & 0.628844810188592 & 0.742310379622817 & 0.371155189811408 \tabularnewline
87 & 0.603137017927938 & 0.793725964144124 & 0.396862982072062 \tabularnewline
88 & 0.582096652166166 & 0.835806695667669 & 0.417903347833834 \tabularnewline
89 & 0.558679888516547 & 0.882640222966905 & 0.441320111483453 \tabularnewline
90 & 0.598157096220026 & 0.803685807559949 & 0.401842903779974 \tabularnewline
91 & 0.570324198017843 & 0.859351603964313 & 0.429675801982157 \tabularnewline
92 & 0.548274253270091 & 0.903451493459818 & 0.451725746729909 \tabularnewline
93 & 0.593708865882408 & 0.812582268235185 & 0.406291134117592 \tabularnewline
94 & 0.552681864005298 & 0.894636271989403 & 0.447318135994702 \tabularnewline
95 & 0.598964236109576 & 0.802071527780849 & 0.401035763890424 \tabularnewline
96 & 0.578804637731024 & 0.842390724537951 & 0.421195362268976 \tabularnewline
97 & 0.597137662109916 & 0.805724675780167 & 0.402862337890084 \tabularnewline
98 & 0.575726942453607 & 0.848546115092785 & 0.424273057546393 \tabularnewline
99 & 0.585142003008531 & 0.829715993982938 & 0.414857996991469 \tabularnewline
100 & 0.636605533662943 & 0.726788932674113 & 0.363394466337057 \tabularnewline
101 & 0.6150445118132 & 0.769910976373599 & 0.384955488186799 \tabularnewline
102 & 0.581811070804592 & 0.836377858390816 & 0.418188929195408 \tabularnewline
103 & 0.568267848201777 & 0.863464303596445 & 0.431732151798223 \tabularnewline
104 & 0.526728934156501 & 0.946542131686997 & 0.473271065843499 \tabularnewline
105 & 0.489010152696447 & 0.978020305392894 & 0.510989847303553 \tabularnewline
106 & 0.484085090515923 & 0.968170181031847 & 0.515914909484077 \tabularnewline
107 & 0.443251196901721 & 0.886502393803443 & 0.556748803098279 \tabularnewline
108 & 0.423113051125958 & 0.846226102251915 & 0.576886948874042 \tabularnewline
109 & 0.420705663313017 & 0.841411326626033 & 0.579294336686983 \tabularnewline
110 & 0.397812041623197 & 0.795624083246394 & 0.602187958376803 \tabularnewline
111 & 0.364481751868533 & 0.728963503737066 & 0.635518248131467 \tabularnewline
112 & 0.331334939643604 & 0.662669879287209 & 0.668665060356396 \tabularnewline
113 & 0.345232510020782 & 0.690465020041565 & 0.654767489979218 \tabularnewline
114 & 0.307222544628448 & 0.614445089256896 & 0.692777455371552 \tabularnewline
115 & 0.285053235851774 & 0.570106471703549 & 0.714946764148226 \tabularnewline
116 & 0.25677966398057 & 0.51355932796114 & 0.74322033601943 \tabularnewline
117 & 0.233523774618624 & 0.467047549237247 & 0.766476225381376 \tabularnewline
118 & 0.217740954102276 & 0.435481908204553 & 0.782259045897724 \tabularnewline
119 & 0.269191025196724 & 0.538382050393449 & 0.730808974803276 \tabularnewline
120 & 0.277529935878483 & 0.555059871756966 & 0.722470064121517 \tabularnewline
121 & 0.277320878782892 & 0.554641757565783 & 0.722679121217108 \tabularnewline
122 & 0.263975897680145 & 0.52795179536029 & 0.736024102319855 \tabularnewline
123 & 0.464493586812371 & 0.928987173624743 & 0.535506413187629 \tabularnewline
124 & 0.455616252820708 & 0.911232505641415 & 0.544383747179292 \tabularnewline
125 & 0.526686622032022 & 0.946626755935957 & 0.473313377967978 \tabularnewline
126 & 0.555556359736652 & 0.888887280526697 & 0.444443640263349 \tabularnewline
127 & 0.548386250229047 & 0.903227499541907 & 0.451613749770953 \tabularnewline
128 & 0.639028595979682 & 0.721942808040635 & 0.360971404020318 \tabularnewline
129 & 0.633150145505531 & 0.733699708988937 & 0.366849854494469 \tabularnewline
130 & 0.599330417263779 & 0.801339165472442 & 0.400669582736221 \tabularnewline
131 & 0.57152852804158 & 0.85694294391684 & 0.42847147195842 \tabularnewline
132 & 0.52882821339049 & 0.94234357321902 & 0.47117178660951 \tabularnewline
133 & 0.510002059293768 & 0.979995881412465 & 0.489997940706232 \tabularnewline
134 & 0.478171357018699 & 0.956342714037397 & 0.521828642981301 \tabularnewline
135 & 0.440904601392002 & 0.881809202784004 & 0.559095398607998 \tabularnewline
136 & 0.443379760096956 & 0.886759520193912 & 0.556620239903044 \tabularnewline
137 & 0.399074665778718 & 0.798149331557436 & 0.600925334221282 \tabularnewline
138 & 0.365477951036707 & 0.730955902073413 & 0.634522048963293 \tabularnewline
139 & 0.347450787487511 & 0.694901574975021 & 0.65254921251249 \tabularnewline
140 & 0.307377448116169 & 0.614754896232339 & 0.69262255188383 \tabularnewline
141 & 0.271663303482737 & 0.543326606965473 & 0.728336696517264 \tabularnewline
142 & 0.285226503807552 & 0.570453007615104 & 0.714773496192448 \tabularnewline
143 & 0.24970828781133 & 0.499416575622659 & 0.75029171218867 \tabularnewline
144 & 0.265228817331314 & 0.530457634662628 & 0.734771182668686 \tabularnewline
145 & 0.411895223048801 & 0.823790446097602 & 0.588104776951199 \tabularnewline
146 & 0.381116633794084 & 0.762233267588168 & 0.618883366205916 \tabularnewline
147 & 0.399774874840253 & 0.799549749680507 & 0.600225125159747 \tabularnewline
148 & 0.359055332719995 & 0.71811066543999 & 0.640944667280005 \tabularnewline
149 & 0.315050496231318 & 0.630100992462635 & 0.684949503768682 \tabularnewline
150 & 0.273438453556903 & 0.546876907113807 & 0.726561546443097 \tabularnewline
151 & 0.237202866961541 & 0.474405733923083 & 0.762797133038459 \tabularnewline
152 & 0.396279801129488 & 0.792559602258975 & 0.603720198870512 \tabularnewline
153 & 0.346859501475779 & 0.693719002951557 & 0.653140498524222 \tabularnewline
154 & 0.316026060615208 & 0.632052121230417 & 0.683973939384792 \tabularnewline
155 & 0.27269238042869 & 0.54538476085738 & 0.72730761957131 \tabularnewline
156 & 0.237921187879064 & 0.475842375758129 & 0.762078812120936 \tabularnewline
157 & 0.240857987269945 & 0.48171597453989 & 0.759142012730055 \tabularnewline
158 & 0.237286415828547 & 0.474572831657093 & 0.762713584171453 \tabularnewline
159 & 0.198810899612474 & 0.397621799224949 & 0.801189100387526 \tabularnewline
160 & 0.162758181212426 & 0.325516362424851 & 0.837241818787574 \tabularnewline
161 & 0.137037214281066 & 0.274074428562132 & 0.862962785718934 \tabularnewline
162 & 0.115426928411025 & 0.230853856822051 & 0.884573071588975 \tabularnewline
163 & 0.121747622768038 & 0.243495245536075 & 0.878252377231962 \tabularnewline
164 & 0.104749075173166 & 0.209498150346333 & 0.895250924826834 \tabularnewline
165 & 0.125783960178131 & 0.251567920356262 & 0.874216039821869 \tabularnewline
166 & 0.114061149506233 & 0.228122299012467 & 0.885938850493767 \tabularnewline
167 & 0.164948181453491 & 0.329896362906983 & 0.835051818546509 \tabularnewline
168 & 0.203021562875422 & 0.406043125750844 & 0.796978437124578 \tabularnewline
169 & 0.195594283216885 & 0.391188566433771 & 0.804405716783115 \tabularnewline
170 & 0.301937208160486 & 0.603874416320972 & 0.698062791839514 \tabularnewline
171 & 0.318987246080777 & 0.637974492161553 & 0.681012753919223 \tabularnewline
172 & 0.259062762326213 & 0.518125524652425 & 0.740937237673787 \tabularnewline
173 & 0.207231852849931 & 0.414463705699862 & 0.792768147150069 \tabularnewline
174 & 0.181438398922317 & 0.362876797844633 & 0.818561601077683 \tabularnewline
175 & 0.185702687127536 & 0.371405374255073 & 0.814297312872464 \tabularnewline
176 & 0.156347084979167 & 0.312694169958333 & 0.843652915020833 \tabularnewline
177 & 0.116017415044117 & 0.232034830088234 & 0.883982584955883 \tabularnewline
178 & 0.102060594096149 & 0.204121188192298 & 0.897939405903851 \tabularnewline
179 & 0.0901741680289291 & 0.180348336057858 & 0.90982583197107 \tabularnewline
180 & 0.135166221722372 & 0.270332443444744 & 0.864833778277628 \tabularnewline
181 & 0.111534490222688 & 0.223068980445376 & 0.888465509777312 \tabularnewline
182 & 0.0772631105673212 & 0.154526221134642 & 0.922736889432679 \tabularnewline
183 & 0.0540291440100027 & 0.108058288020005 & 0.945970855989997 \tabularnewline
184 & 0.0512555479809749 & 0.10251109596195 & 0.948744452019025 \tabularnewline
185 & 0.0865484004555814 & 0.173096800911163 & 0.913451599544419 \tabularnewline
186 & 0.0448417369653801 & 0.0896834739307602 & 0.95515826303462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0114589232736245[/C][C]0.022917846547249[/C][C]0.988541076726375[/C][/ROW]
[ROW][C]10[/C][C]0.00558025508464657[/C][C]0.0111605101692931[/C][C]0.994419744915353[/C][/ROW]
[ROW][C]11[/C][C]0.00383039495138543[/C][C]0.00766078990277086[/C][C]0.996169605048615[/C][/ROW]
[ROW][C]12[/C][C]0.00144688169110891[/C][C]0.00289376338221783[/C][C]0.99855311830889[/C][/ROW]
[ROW][C]13[/C][C]0.000406829690622745[/C][C]0.00081365938124549[/C][C]0.999593170309377[/C][/ROW]
[ROW][C]14[/C][C]0.000110845320022862[/C][C]0.000221690640045724[/C][C]0.999889154679977[/C][/ROW]
[ROW][C]15[/C][C]7.2342061789211e-05[/C][C]0.000144684123578422[/C][C]0.99992765793821[/C][/ROW]
[ROW][C]16[/C][C]1.75447938266853e-05[/C][C]3.50895876533707e-05[/C][C]0.999982455206173[/C][/ROW]
[ROW][C]17[/C][C]5.37635136478093e-06[/C][C]1.07527027295619e-05[/C][C]0.999994623648635[/C][/ROW]
[ROW][C]18[/C][C]2.89050727484676e-06[/C][C]5.78101454969353e-06[/C][C]0.999997109492725[/C][/ROW]
[ROW][C]19[/C][C]1.20016921465062e-06[/C][C]2.40033842930125e-06[/C][C]0.999998799830785[/C][/ROW]
[ROW][C]20[/C][C]9.13359193218518e-07[/C][C]1.82671838643704e-06[/C][C]0.999999086640807[/C][/ROW]
[ROW][C]21[/C][C]2.61232875895227e-07[/C][C]5.22465751790455e-07[/C][C]0.999999738767124[/C][/ROW]
[ROW][C]22[/C][C]2.52252598181451e-07[/C][C]5.04505196362902e-07[/C][C]0.999999747747402[/C][/ROW]
[ROW][C]23[/C][C]7.2864035954946e-08[/C][C]1.45728071909892e-07[/C][C]0.999999927135964[/C][/ROW]
[ROW][C]24[/C][C]1.84663738701328e-08[/C][C]3.69327477402656e-08[/C][C]0.999999981533626[/C][/ROW]
[ROW][C]25[/C][C]5.44503040070167e-09[/C][C]1.08900608014033e-08[/C][C]0.99999999455497[/C][/ROW]
[ROW][C]26[/C][C]4.41030311973867e-09[/C][C]8.82060623947733e-09[/C][C]0.999999995589697[/C][/ROW]
[ROW][C]27[/C][C]1.95896596257068e-09[/C][C]3.91793192514135e-09[/C][C]0.999999998041034[/C][/ROW]
[ROW][C]28[/C][C]5.15437507545988e-10[/C][C]1.03087501509198e-09[/C][C]0.999999999484563[/C][/ROW]
[ROW][C]29[/C][C]1.68819821438689e-09[/C][C]3.37639642877377e-09[/C][C]0.999999998311802[/C][/ROW]
[ROW][C]30[/C][C]5.9772883698613e-10[/C][C]1.19545767397226e-09[/C][C]0.999999999402271[/C][/ROW]
[ROW][C]31[/C][C]0.000150681664532755[/C][C]0.00030136332906551[/C][C]0.999849318335467[/C][/ROW]
[ROW][C]32[/C][C]8.0415756876931e-05[/C][C]0.000160831513753862[/C][C]0.999919584243123[/C][/ROW]
[ROW][C]33[/C][C]0.000365907109930215[/C][C]0.00073181421986043[/C][C]0.99963409289007[/C][/ROW]
[ROW][C]34[/C][C]0.00164732958504155[/C][C]0.00329465917008311[/C][C]0.998352670414958[/C][/ROW]
[ROW][C]35[/C][C]0.00266332112442352[/C][C]0.00532664224884704[/C][C]0.997336678875577[/C][/ROW]
[ROW][C]36[/C][C]0.00275854827666059[/C][C]0.00551709655332117[/C][C]0.99724145172334[/C][/ROW]
[ROW][C]37[/C][C]0.0045700735963822[/C][C]0.0091401471927644[/C][C]0.995429926403618[/C][/ROW]
[ROW][C]38[/C][C]0.0244512042985658[/C][C]0.0489024085971317[/C][C]0.975548795701434[/C][/ROW]
[ROW][C]39[/C][C]0.0183419130615764[/C][C]0.0366838261231528[/C][C]0.981658086938424[/C][/ROW]
[ROW][C]40[/C][C]0.0146526036778114[/C][C]0.0293052073556228[/C][C]0.985347396322189[/C][/ROW]
[ROW][C]41[/C][C]0.0446198354902716[/C][C]0.0892396709805433[/C][C]0.955380164509728[/C][/ROW]
[ROW][C]42[/C][C]0.154352659497981[/C][C]0.308705318995963[/C][C]0.845647340502019[/C][/ROW]
[ROW][C]43[/C][C]0.194495406489687[/C][C]0.388990812979374[/C][C]0.805504593510313[/C][/ROW]
[ROW][C]44[/C][C]0.379424675276356[/C][C]0.758849350552712[/C][C]0.620575324723644[/C][/ROW]
[ROW][C]45[/C][C]0.336592955236531[/C][C]0.673185910473063[/C][C]0.663407044763469[/C][/ROW]
[ROW][C]46[/C][C]0.78023838126308[/C][C]0.43952323747384[/C][C]0.21976161873692[/C][/ROW]
[ROW][C]47[/C][C]0.770510961115485[/C][C]0.45897807776903[/C][C]0.229489038884515[/C][/ROW]
[ROW][C]48[/C][C]0.743121907164485[/C][C]0.51375618567103[/C][C]0.256878092835515[/C][/ROW]
[ROW][C]49[/C][C]0.84264550533692[/C][C]0.31470898932616[/C][C]0.15735449466308[/C][/ROW]
[ROW][C]50[/C][C]0.815705503819519[/C][C]0.368588992360962[/C][C]0.184294496180481[/C][/ROW]
[ROW][C]51[/C][C]0.795694369030286[/C][C]0.408611261939429[/C][C]0.204305630969714[/C][/ROW]
[ROW][C]52[/C][C]0.774454530019864[/C][C]0.451090939960273[/C][C]0.225545469980136[/C][/ROW]
[ROW][C]53[/C][C]0.738658209733368[/C][C]0.522683580533264[/C][C]0.261341790266632[/C][/ROW]
[ROW][C]54[/C][C]0.865208577888779[/C][C]0.269582844222443[/C][C]0.134791422111221[/C][/ROW]
[ROW][C]55[/C][C]0.85521114506837[/C][C]0.289577709863258[/C][C]0.144788854931629[/C][/ROW]
[ROW][C]56[/C][C]0.856080756588507[/C][C]0.287838486822985[/C][C]0.143919243411493[/C][/ROW]
[ROW][C]57[/C][C]0.844351110590967[/C][C]0.311297778818066[/C][C]0.155648889409033[/C][/ROW]
[ROW][C]58[/C][C]0.818575428099169[/C][C]0.362849143801662[/C][C]0.181424571900831[/C][/ROW]
[ROW][C]59[/C][C]0.791391825211841[/C][C]0.417216349576318[/C][C]0.208608174788159[/C][/ROW]
[ROW][C]60[/C][C]0.767627451412294[/C][C]0.464745097175412[/C][C]0.232372548587706[/C][/ROW]
[ROW][C]61[/C][C]0.76372558428232[/C][C]0.472548831435361[/C][C]0.23627441571768[/C][/ROW]
[ROW][C]62[/C][C]0.760727649162135[/C][C]0.478544701675729[/C][C]0.239272350837865[/C][/ROW]
[ROW][C]63[/C][C]0.792621319161183[/C][C]0.414757361677634[/C][C]0.207378680838817[/C][/ROW]
[ROW][C]64[/C][C]0.785498182201388[/C][C]0.429003635597224[/C][C]0.214501817798612[/C][/ROW]
[ROW][C]65[/C][C]0.75886385283467[/C][C]0.482272294330659[/C][C]0.24113614716533[/C][/ROW]
[ROW][C]66[/C][C]0.72969852274891[/C][C]0.54060295450218[/C][C]0.27030147725109[/C][/ROW]
[ROW][C]67[/C][C]0.697688722205181[/C][C]0.604622555589638[/C][C]0.302311277794819[/C][/ROW]
[ROW][C]68[/C][C]0.67050916972791[/C][C]0.658981660544181[/C][C]0.329490830272091[/C][/ROW]
[ROW][C]69[/C][C]0.634003336460959[/C][C]0.731993327078082[/C][C]0.365996663539041[/C][/ROW]
[ROW][C]70[/C][C]0.599289230414864[/C][C]0.801421539170271[/C][C]0.400710769585136[/C][/ROW]
[ROW][C]71[/C][C]0.565225622782044[/C][C]0.869548754435912[/C][C]0.434774377217956[/C][/ROW]
[ROW][C]72[/C][C]0.525981248344482[/C][C]0.948037503311036[/C][C]0.474018751655518[/C][/ROW]
[ROW][C]73[/C][C]0.557120398905477[/C][C]0.885759202189046[/C][C]0.442879601094523[/C][/ROW]
[ROW][C]74[/C][C]0.531071433194677[/C][C]0.937857133610647[/C][C]0.468928566805323[/C][/ROW]
[ROW][C]75[/C][C]0.496724543571677[/C][C]0.993449087143354[/C][C]0.503275456428323[/C][/ROW]
[ROW][C]76[/C][C]0.497510218661856[/C][C]0.995020437323713[/C][C]0.502489781338144[/C][/ROW]
[ROW][C]77[/C][C]0.502629175223135[/C][C]0.99474164955373[/C][C]0.497370824776865[/C][/ROW]
[ROW][C]78[/C][C]0.506425799985833[/C][C]0.987148400028335[/C][C]0.493574200014167[/C][/ROW]
[ROW][C]79[/C][C]0.534512814701131[/C][C]0.930974370597737[/C][C]0.465487185298869[/C][/ROW]
[ROW][C]80[/C][C]0.597339585083252[/C][C]0.805320829833495[/C][C]0.402660414916748[/C][/ROW]
[ROW][C]81[/C][C]0.567974395886876[/C][C]0.864051208226247[/C][C]0.432025604113124[/C][/ROW]
[ROW][C]82[/C][C]0.660230495497843[/C][C]0.679539009004315[/C][C]0.339769504502157[/C][/ROW]
[ROW][C]83[/C][C]0.642377241358703[/C][C]0.715245517282593[/C][C]0.357622758641297[/C][/ROW]
[ROW][C]84[/C][C]0.622147061998002[/C][C]0.755705876003997[/C][C]0.377852938001998[/C][/ROW]
[ROW][C]85[/C][C]0.611980370395952[/C][C]0.776039259208096[/C][C]0.388019629604048[/C][/ROW]
[ROW][C]86[/C][C]0.628844810188592[/C][C]0.742310379622817[/C][C]0.371155189811408[/C][/ROW]
[ROW][C]87[/C][C]0.603137017927938[/C][C]0.793725964144124[/C][C]0.396862982072062[/C][/ROW]
[ROW][C]88[/C][C]0.582096652166166[/C][C]0.835806695667669[/C][C]0.417903347833834[/C][/ROW]
[ROW][C]89[/C][C]0.558679888516547[/C][C]0.882640222966905[/C][C]0.441320111483453[/C][/ROW]
[ROW][C]90[/C][C]0.598157096220026[/C][C]0.803685807559949[/C][C]0.401842903779974[/C][/ROW]
[ROW][C]91[/C][C]0.570324198017843[/C][C]0.859351603964313[/C][C]0.429675801982157[/C][/ROW]
[ROW][C]92[/C][C]0.548274253270091[/C][C]0.903451493459818[/C][C]0.451725746729909[/C][/ROW]
[ROW][C]93[/C][C]0.593708865882408[/C][C]0.812582268235185[/C][C]0.406291134117592[/C][/ROW]
[ROW][C]94[/C][C]0.552681864005298[/C][C]0.894636271989403[/C][C]0.447318135994702[/C][/ROW]
[ROW][C]95[/C][C]0.598964236109576[/C][C]0.802071527780849[/C][C]0.401035763890424[/C][/ROW]
[ROW][C]96[/C][C]0.578804637731024[/C][C]0.842390724537951[/C][C]0.421195362268976[/C][/ROW]
[ROW][C]97[/C][C]0.597137662109916[/C][C]0.805724675780167[/C][C]0.402862337890084[/C][/ROW]
[ROW][C]98[/C][C]0.575726942453607[/C][C]0.848546115092785[/C][C]0.424273057546393[/C][/ROW]
[ROW][C]99[/C][C]0.585142003008531[/C][C]0.829715993982938[/C][C]0.414857996991469[/C][/ROW]
[ROW][C]100[/C][C]0.636605533662943[/C][C]0.726788932674113[/C][C]0.363394466337057[/C][/ROW]
[ROW][C]101[/C][C]0.6150445118132[/C][C]0.769910976373599[/C][C]0.384955488186799[/C][/ROW]
[ROW][C]102[/C][C]0.581811070804592[/C][C]0.836377858390816[/C][C]0.418188929195408[/C][/ROW]
[ROW][C]103[/C][C]0.568267848201777[/C][C]0.863464303596445[/C][C]0.431732151798223[/C][/ROW]
[ROW][C]104[/C][C]0.526728934156501[/C][C]0.946542131686997[/C][C]0.473271065843499[/C][/ROW]
[ROW][C]105[/C][C]0.489010152696447[/C][C]0.978020305392894[/C][C]0.510989847303553[/C][/ROW]
[ROW][C]106[/C][C]0.484085090515923[/C][C]0.968170181031847[/C][C]0.515914909484077[/C][/ROW]
[ROW][C]107[/C][C]0.443251196901721[/C][C]0.886502393803443[/C][C]0.556748803098279[/C][/ROW]
[ROW][C]108[/C][C]0.423113051125958[/C][C]0.846226102251915[/C][C]0.576886948874042[/C][/ROW]
[ROW][C]109[/C][C]0.420705663313017[/C][C]0.841411326626033[/C][C]0.579294336686983[/C][/ROW]
[ROW][C]110[/C][C]0.397812041623197[/C][C]0.795624083246394[/C][C]0.602187958376803[/C][/ROW]
[ROW][C]111[/C][C]0.364481751868533[/C][C]0.728963503737066[/C][C]0.635518248131467[/C][/ROW]
[ROW][C]112[/C][C]0.331334939643604[/C][C]0.662669879287209[/C][C]0.668665060356396[/C][/ROW]
[ROW][C]113[/C][C]0.345232510020782[/C][C]0.690465020041565[/C][C]0.654767489979218[/C][/ROW]
[ROW][C]114[/C][C]0.307222544628448[/C][C]0.614445089256896[/C][C]0.692777455371552[/C][/ROW]
[ROW][C]115[/C][C]0.285053235851774[/C][C]0.570106471703549[/C][C]0.714946764148226[/C][/ROW]
[ROW][C]116[/C][C]0.25677966398057[/C][C]0.51355932796114[/C][C]0.74322033601943[/C][/ROW]
[ROW][C]117[/C][C]0.233523774618624[/C][C]0.467047549237247[/C][C]0.766476225381376[/C][/ROW]
[ROW][C]118[/C][C]0.217740954102276[/C][C]0.435481908204553[/C][C]0.782259045897724[/C][/ROW]
[ROW][C]119[/C][C]0.269191025196724[/C][C]0.538382050393449[/C][C]0.730808974803276[/C][/ROW]
[ROW][C]120[/C][C]0.277529935878483[/C][C]0.555059871756966[/C][C]0.722470064121517[/C][/ROW]
[ROW][C]121[/C][C]0.277320878782892[/C][C]0.554641757565783[/C][C]0.722679121217108[/C][/ROW]
[ROW][C]122[/C][C]0.263975897680145[/C][C]0.52795179536029[/C][C]0.736024102319855[/C][/ROW]
[ROW][C]123[/C][C]0.464493586812371[/C][C]0.928987173624743[/C][C]0.535506413187629[/C][/ROW]
[ROW][C]124[/C][C]0.455616252820708[/C][C]0.911232505641415[/C][C]0.544383747179292[/C][/ROW]
[ROW][C]125[/C][C]0.526686622032022[/C][C]0.946626755935957[/C][C]0.473313377967978[/C][/ROW]
[ROW][C]126[/C][C]0.555556359736652[/C][C]0.888887280526697[/C][C]0.444443640263349[/C][/ROW]
[ROW][C]127[/C][C]0.548386250229047[/C][C]0.903227499541907[/C][C]0.451613749770953[/C][/ROW]
[ROW][C]128[/C][C]0.639028595979682[/C][C]0.721942808040635[/C][C]0.360971404020318[/C][/ROW]
[ROW][C]129[/C][C]0.633150145505531[/C][C]0.733699708988937[/C][C]0.366849854494469[/C][/ROW]
[ROW][C]130[/C][C]0.599330417263779[/C][C]0.801339165472442[/C][C]0.400669582736221[/C][/ROW]
[ROW][C]131[/C][C]0.57152852804158[/C][C]0.85694294391684[/C][C]0.42847147195842[/C][/ROW]
[ROW][C]132[/C][C]0.52882821339049[/C][C]0.94234357321902[/C][C]0.47117178660951[/C][/ROW]
[ROW][C]133[/C][C]0.510002059293768[/C][C]0.979995881412465[/C][C]0.489997940706232[/C][/ROW]
[ROW][C]134[/C][C]0.478171357018699[/C][C]0.956342714037397[/C][C]0.521828642981301[/C][/ROW]
[ROW][C]135[/C][C]0.440904601392002[/C][C]0.881809202784004[/C][C]0.559095398607998[/C][/ROW]
[ROW][C]136[/C][C]0.443379760096956[/C][C]0.886759520193912[/C][C]0.556620239903044[/C][/ROW]
[ROW][C]137[/C][C]0.399074665778718[/C][C]0.798149331557436[/C][C]0.600925334221282[/C][/ROW]
[ROW][C]138[/C][C]0.365477951036707[/C][C]0.730955902073413[/C][C]0.634522048963293[/C][/ROW]
[ROW][C]139[/C][C]0.347450787487511[/C][C]0.694901574975021[/C][C]0.65254921251249[/C][/ROW]
[ROW][C]140[/C][C]0.307377448116169[/C][C]0.614754896232339[/C][C]0.69262255188383[/C][/ROW]
[ROW][C]141[/C][C]0.271663303482737[/C][C]0.543326606965473[/C][C]0.728336696517264[/C][/ROW]
[ROW][C]142[/C][C]0.285226503807552[/C][C]0.570453007615104[/C][C]0.714773496192448[/C][/ROW]
[ROW][C]143[/C][C]0.24970828781133[/C][C]0.499416575622659[/C][C]0.75029171218867[/C][/ROW]
[ROW][C]144[/C][C]0.265228817331314[/C][C]0.530457634662628[/C][C]0.734771182668686[/C][/ROW]
[ROW][C]145[/C][C]0.411895223048801[/C][C]0.823790446097602[/C][C]0.588104776951199[/C][/ROW]
[ROW][C]146[/C][C]0.381116633794084[/C][C]0.762233267588168[/C][C]0.618883366205916[/C][/ROW]
[ROW][C]147[/C][C]0.399774874840253[/C][C]0.799549749680507[/C][C]0.600225125159747[/C][/ROW]
[ROW][C]148[/C][C]0.359055332719995[/C][C]0.71811066543999[/C][C]0.640944667280005[/C][/ROW]
[ROW][C]149[/C][C]0.315050496231318[/C][C]0.630100992462635[/C][C]0.684949503768682[/C][/ROW]
[ROW][C]150[/C][C]0.273438453556903[/C][C]0.546876907113807[/C][C]0.726561546443097[/C][/ROW]
[ROW][C]151[/C][C]0.237202866961541[/C][C]0.474405733923083[/C][C]0.762797133038459[/C][/ROW]
[ROW][C]152[/C][C]0.396279801129488[/C][C]0.792559602258975[/C][C]0.603720198870512[/C][/ROW]
[ROW][C]153[/C][C]0.346859501475779[/C][C]0.693719002951557[/C][C]0.653140498524222[/C][/ROW]
[ROW][C]154[/C][C]0.316026060615208[/C][C]0.632052121230417[/C][C]0.683973939384792[/C][/ROW]
[ROW][C]155[/C][C]0.27269238042869[/C][C]0.54538476085738[/C][C]0.72730761957131[/C][/ROW]
[ROW][C]156[/C][C]0.237921187879064[/C][C]0.475842375758129[/C][C]0.762078812120936[/C][/ROW]
[ROW][C]157[/C][C]0.240857987269945[/C][C]0.48171597453989[/C][C]0.759142012730055[/C][/ROW]
[ROW][C]158[/C][C]0.237286415828547[/C][C]0.474572831657093[/C][C]0.762713584171453[/C][/ROW]
[ROW][C]159[/C][C]0.198810899612474[/C][C]0.397621799224949[/C][C]0.801189100387526[/C][/ROW]
[ROW][C]160[/C][C]0.162758181212426[/C][C]0.325516362424851[/C][C]0.837241818787574[/C][/ROW]
[ROW][C]161[/C][C]0.137037214281066[/C][C]0.274074428562132[/C][C]0.862962785718934[/C][/ROW]
[ROW][C]162[/C][C]0.115426928411025[/C][C]0.230853856822051[/C][C]0.884573071588975[/C][/ROW]
[ROW][C]163[/C][C]0.121747622768038[/C][C]0.243495245536075[/C][C]0.878252377231962[/C][/ROW]
[ROW][C]164[/C][C]0.104749075173166[/C][C]0.209498150346333[/C][C]0.895250924826834[/C][/ROW]
[ROW][C]165[/C][C]0.125783960178131[/C][C]0.251567920356262[/C][C]0.874216039821869[/C][/ROW]
[ROW][C]166[/C][C]0.114061149506233[/C][C]0.228122299012467[/C][C]0.885938850493767[/C][/ROW]
[ROW][C]167[/C][C]0.164948181453491[/C][C]0.329896362906983[/C][C]0.835051818546509[/C][/ROW]
[ROW][C]168[/C][C]0.203021562875422[/C][C]0.406043125750844[/C][C]0.796978437124578[/C][/ROW]
[ROW][C]169[/C][C]0.195594283216885[/C][C]0.391188566433771[/C][C]0.804405716783115[/C][/ROW]
[ROW][C]170[/C][C]0.301937208160486[/C][C]0.603874416320972[/C][C]0.698062791839514[/C][/ROW]
[ROW][C]171[/C][C]0.318987246080777[/C][C]0.637974492161553[/C][C]0.681012753919223[/C][/ROW]
[ROW][C]172[/C][C]0.259062762326213[/C][C]0.518125524652425[/C][C]0.740937237673787[/C][/ROW]
[ROW][C]173[/C][C]0.207231852849931[/C][C]0.414463705699862[/C][C]0.792768147150069[/C][/ROW]
[ROW][C]174[/C][C]0.181438398922317[/C][C]0.362876797844633[/C][C]0.818561601077683[/C][/ROW]
[ROW][C]175[/C][C]0.185702687127536[/C][C]0.371405374255073[/C][C]0.814297312872464[/C][/ROW]
[ROW][C]176[/C][C]0.156347084979167[/C][C]0.312694169958333[/C][C]0.843652915020833[/C][/ROW]
[ROW][C]177[/C][C]0.116017415044117[/C][C]0.232034830088234[/C][C]0.883982584955883[/C][/ROW]
[ROW][C]178[/C][C]0.102060594096149[/C][C]0.204121188192298[/C][C]0.897939405903851[/C][/ROW]
[ROW][C]179[/C][C]0.0901741680289291[/C][C]0.180348336057858[/C][C]0.90982583197107[/C][/ROW]
[ROW][C]180[/C][C]0.135166221722372[/C][C]0.270332443444744[/C][C]0.864833778277628[/C][/ROW]
[ROW][C]181[/C][C]0.111534490222688[/C][C]0.223068980445376[/C][C]0.888465509777312[/C][/ROW]
[ROW][C]182[/C][C]0.0772631105673212[/C][C]0.154526221134642[/C][C]0.922736889432679[/C][/ROW]
[ROW][C]183[/C][C]0.0540291440100027[/C][C]0.108058288020005[/C][C]0.945970855989997[/C][/ROW]
[ROW][C]184[/C][C]0.0512555479809749[/C][C]0.10251109596195[/C][C]0.948744452019025[/C][/ROW]
[ROW][C]185[/C][C]0.0865484004555814[/C][C]0.173096800911163[/C][C]0.913451599544419[/C][/ROW]
[ROW][C]186[/C][C]0.0448417369653801[/C][C]0.0896834739307602[/C][C]0.95515826303462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.01145892327362450.0229178465472490.988541076726375
100.005580255084646570.01116051016929310.994419744915353
110.003830394951385430.007660789902770860.996169605048615
120.001446881691108910.002893763382217830.99855311830889
130.0004068296906227450.000813659381245490.999593170309377
140.0001108453200228620.0002216906400457240.999889154679977
157.2342061789211e-050.0001446841235784220.99992765793821
161.75447938266853e-053.50895876533707e-050.999982455206173
175.37635136478093e-061.07527027295619e-050.999994623648635
182.89050727484676e-065.78101454969353e-060.999997109492725
191.20016921465062e-062.40033842930125e-060.999998799830785
209.13359193218518e-071.82671838643704e-060.999999086640807
212.61232875895227e-075.22465751790455e-070.999999738767124
222.52252598181451e-075.04505196362902e-070.999999747747402
237.2864035954946e-081.45728071909892e-070.999999927135964
241.84663738701328e-083.69327477402656e-080.999999981533626
255.44503040070167e-091.08900608014033e-080.99999999455497
264.41030311973867e-098.82060623947733e-090.999999995589697
271.95896596257068e-093.91793192514135e-090.999999998041034
285.15437507545988e-101.03087501509198e-090.999999999484563
291.68819821438689e-093.37639642877377e-090.999999998311802
305.9772883698613e-101.19545767397226e-090.999999999402271
310.0001506816645327550.000301363329065510.999849318335467
328.0415756876931e-050.0001608315137538620.999919584243123
330.0003659071099302150.000731814219860430.99963409289007
340.001647329585041550.003294659170083110.998352670414958
350.002663321124423520.005326642248847040.997336678875577
360.002758548276660590.005517096553321170.99724145172334
370.00457007359638220.00914014719276440.995429926403618
380.02445120429856580.04890240859713170.975548795701434
390.01834191306157640.03668382612315280.981658086938424
400.01465260367781140.02930520735562280.985347396322189
410.04461983549027160.08923967098054330.955380164509728
420.1543526594979810.3087053189959630.845647340502019
430.1944954064896870.3889908129793740.805504593510313
440.3794246752763560.7588493505527120.620575324723644
450.3365929552365310.6731859104730630.663407044763469
460.780238381263080.439523237473840.21976161873692
470.7705109611154850.458978077769030.229489038884515
480.7431219071644850.513756185671030.256878092835515
490.842645505336920.314708989326160.15735449466308
500.8157055038195190.3685889923609620.184294496180481
510.7956943690302860.4086112619394290.204305630969714
520.7744545300198640.4510909399602730.225545469980136
530.7386582097333680.5226835805332640.261341790266632
540.8652085778887790.2695828442224430.134791422111221
550.855211145068370.2895777098632580.144788854931629
560.8560807565885070.2878384868229850.143919243411493
570.8443511105909670.3112977788180660.155648889409033
580.8185754280991690.3628491438016620.181424571900831
590.7913918252118410.4172163495763180.208608174788159
600.7676274514122940.4647450971754120.232372548587706
610.763725584282320.4725488314353610.23627441571768
620.7607276491621350.4785447016757290.239272350837865
630.7926213191611830.4147573616776340.207378680838817
640.7854981822013880.4290036355972240.214501817798612
650.758863852834670.4822722943306590.24113614716533
660.729698522748910.540602954502180.27030147725109
670.6976887222051810.6046225555896380.302311277794819
680.670509169727910.6589816605441810.329490830272091
690.6340033364609590.7319933270780820.365996663539041
700.5992892304148640.8014215391702710.400710769585136
710.5652256227820440.8695487544359120.434774377217956
720.5259812483444820.9480375033110360.474018751655518
730.5571203989054770.8857592021890460.442879601094523
740.5310714331946770.9378571336106470.468928566805323
750.4967245435716770.9934490871433540.503275456428323
760.4975102186618560.9950204373237130.502489781338144
770.5026291752231350.994741649553730.497370824776865
780.5064257999858330.9871484000283350.493574200014167
790.5345128147011310.9309743705977370.465487185298869
800.5973395850832520.8053208298334950.402660414916748
810.5679743958868760.8640512082262470.432025604113124
820.6602304954978430.6795390090043150.339769504502157
830.6423772413587030.7152455172825930.357622758641297
840.6221470619980020.7557058760039970.377852938001998
850.6119803703959520.7760392592080960.388019629604048
860.6288448101885920.7423103796228170.371155189811408
870.6031370179279380.7937259641441240.396862982072062
880.5820966521661660.8358066956676690.417903347833834
890.5586798885165470.8826402229669050.441320111483453
900.5981570962200260.8036858075599490.401842903779974
910.5703241980178430.8593516039643130.429675801982157
920.5482742532700910.9034514934598180.451725746729909
930.5937088658824080.8125822682351850.406291134117592
940.5526818640052980.8946362719894030.447318135994702
950.5989642361095760.8020715277808490.401035763890424
960.5788046377310240.8423907245379510.421195362268976
970.5971376621099160.8057246757801670.402862337890084
980.5757269424536070.8485461150927850.424273057546393
990.5851420030085310.8297159939829380.414857996991469
1000.6366055336629430.7267889326741130.363394466337057
1010.61504451181320.7699109763735990.384955488186799
1020.5818110708045920.8363778583908160.418188929195408
1030.5682678482017770.8634643035964450.431732151798223
1040.5267289341565010.9465421316869970.473271065843499
1050.4890101526964470.9780203053928940.510989847303553
1060.4840850905159230.9681701810318470.515914909484077
1070.4432511969017210.8865023938034430.556748803098279
1080.4231130511259580.8462261022519150.576886948874042
1090.4207056633130170.8414113266260330.579294336686983
1100.3978120416231970.7956240832463940.602187958376803
1110.3644817518685330.7289635037370660.635518248131467
1120.3313349396436040.6626698792872090.668665060356396
1130.3452325100207820.6904650200415650.654767489979218
1140.3072225446284480.6144450892568960.692777455371552
1150.2850532358517740.5701064717035490.714946764148226
1160.256779663980570.513559327961140.74322033601943
1170.2335237746186240.4670475492372470.766476225381376
1180.2177409541022760.4354819082045530.782259045897724
1190.2691910251967240.5383820503934490.730808974803276
1200.2775299358784830.5550598717569660.722470064121517
1210.2773208787828920.5546417575657830.722679121217108
1220.2639758976801450.527951795360290.736024102319855
1230.4644935868123710.9289871736247430.535506413187629
1240.4556162528207080.9112325056414150.544383747179292
1250.5266866220320220.9466267559359570.473313377967978
1260.5555563597366520.8888872805266970.444443640263349
1270.5483862502290470.9032274995419070.451613749770953
1280.6390285959796820.7219428080406350.360971404020318
1290.6331501455055310.7336997089889370.366849854494469
1300.5993304172637790.8013391654724420.400669582736221
1310.571528528041580.856942943916840.42847147195842
1320.528828213390490.942343573219020.47117178660951
1330.5100020592937680.9799958814124650.489997940706232
1340.4781713570186990.9563427140373970.521828642981301
1350.4409046013920020.8818092027840040.559095398607998
1360.4433797600969560.8867595201939120.556620239903044
1370.3990746657787180.7981493315574360.600925334221282
1380.3654779510367070.7309559020734130.634522048963293
1390.3474507874875110.6949015749750210.65254921251249
1400.3073774481161690.6147548962323390.69262255188383
1410.2716633034827370.5433266069654730.728336696517264
1420.2852265038075520.5704530076151040.714773496192448
1430.249708287811330.4994165756226590.75029171218867
1440.2652288173313140.5304576346626280.734771182668686
1450.4118952230488010.8237904460976020.588104776951199
1460.3811166337940840.7622332675881680.618883366205916
1470.3997748748402530.7995497496805070.600225125159747
1480.3590553327199950.718110665439990.640944667280005
1490.3150504962313180.6301009924626350.684949503768682
1500.2734384535569030.5468769071138070.726561546443097
1510.2372028669615410.4744057339230830.762797133038459
1520.3962798011294880.7925596022589750.603720198870512
1530.3468595014757790.6937190029515570.653140498524222
1540.3160260606152080.6320521212304170.683973939384792
1550.272692380428690.545384760857380.72730761957131
1560.2379211878790640.4758423757581290.762078812120936
1570.2408579872699450.481715974539890.759142012730055
1580.2372864158285470.4745728316570930.762713584171453
1590.1988108996124740.3976217992249490.801189100387526
1600.1627581812124260.3255163624248510.837241818787574
1610.1370372142810660.2740744285621320.862962785718934
1620.1154269284110250.2308538568220510.884573071588975
1630.1217476227680380.2434952455360750.878252377231962
1640.1047490751731660.2094981503463330.895250924826834
1650.1257839601781310.2515679203562620.874216039821869
1660.1140611495062330.2281222990124670.885938850493767
1670.1649481814534910.3298963629069830.835051818546509
1680.2030215628754220.4060431257508440.796978437124578
1690.1955942832168850.3911885664337710.804405716783115
1700.3019372081604860.6038744163209720.698062791839514
1710.3189872460807770.6379744921615530.681012753919223
1720.2590627623262130.5181255246524250.740937237673787
1730.2072318528499310.4144637056998620.792768147150069
1740.1814383989223170.3628767978446330.818561601077683
1750.1857026871275360.3714053742550730.814297312872464
1760.1563470849791670.3126941699583330.843652915020833
1770.1160174150441170.2320348300882340.883982584955883
1780.1020605940961490.2041211881922980.897939405903851
1790.09017416802892910.1803483360578580.90982583197107
1800.1351662217223720.2703324434447440.864833778277628
1810.1115344902226880.2230689804453760.888465509777312
1820.07726311056732120.1545262211346420.922736889432679
1830.05402914401000270.1080582880200050.945970855989997
1840.05125554798097490.102511095961950.948744452019025
1850.08654840045558140.1730968009111630.913451599544419
1860.04484173696538010.08968347393076020.95515826303462






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.151685393258427NOK
5% type I error level320.179775280898876NOK
10% type I error level340.191011235955056NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 27 & 0.151685393258427 & NOK \tabularnewline
5% type I error level & 32 & 0.179775280898876 & NOK \tabularnewline
10% type I error level & 34 & 0.191011235955056 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116235&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]27[/C][C]0.151685393258427[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.179775280898876[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.191011235955056[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116235&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116235&T=6

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.151685393258427NOK
5% type I error level320.179775280898876NOK
10% type I error level340.191011235955056NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}